Relevant bibliographies by topics / Acoustic speech features

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Acoustic speech features'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Acoustic speech features"

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Masih, Dawa A. A., Nawzad K. Jalal, Manar N. A. Mohammed, and Sulaiman A. Mustafa. "The Assessment of Acoustical Characteristics for Recent Mosque Buildings in Erbil City of Iraq." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 1 (March 1, 2021): 51–66. http://dx.doi.org/10.14500/aro.10784.

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The study of mosque acoustics, concerning acoustical features, sound quality for speech intelligibility, and additional practical acoustic criteria, is commonly overlooked. Acoustic quality is vital to the fundamental use of mosques, in terms of contributing toward prayers and worshippers’ appreciation. This paper undertakes a comparative analysis of the acoustic quality level and the acoustical characteristics for two modern mosque buildings constructed in Erbil city. This work investigates and examines the acoustical quality and performance of these two mosques and their prayer halls through room simulation using ODEON Room Acoustics Software, to assess the degree of speech intelligibility according to acoustic criteria relative to the spatial requirements and design guidelines. The sound pressure level and other room-acoustic indicators, such as reverberation time (T30), early decay time, and speech transmission index, are tested. The outcomes demonstrate the quality of acoustics in the investigated mosques during semi-occupied and fully-occupied circumstances. The results specify that the sound quality within the both mosques is displeasing as the loudspeakers were off.
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Vyaltseva, Darya. "Acoustic Features of Twins’ Speech." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije 16, no. 3 (November 15, 2017): 227–34. http://dx.doi.org/10.15688/jvolsu2.2017.3.24.

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Sepulveda-Sepulveda, Alexander, and German Castellanos-Domínguez. "Time-Frequency Energy Features for Articulator Position Inference on Stop Consonants." Ingeniería y Ciencia 8, no. 16 (November 30, 2012): 37–56. http://dx.doi.org/10.17230/ingciencia.8.16.2.

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Acoustic-to-Articulatory inversion offers new perspectives and interesting applicationsin the speech processing field; however, it remains an open issue. This paper presents a method to estimate the distribution of the articulatory informationcontained in the stop consonants’ acoustics, whose parametrizationis achieved by using the wavelet packet transform. The main focus is on measuringthe relevant acoustic information, in terms of statistical association, forthe inference of the position of critical articulators involved in stop consonantsproduction. The rank correlation Kendall coefficient is used as the relevance measure. The maps of relevant time–frequency features are calculated for theMOCHA–TIMIT database; from which, stop consonants are extracted andanalysed. The proposed method obtains a set of time–frequency components closely related to articulatory phenemenon, which offers a deeper understanding into the relationship between the articulatory and acoustical phenomena.The relevant maps are tested into an acoustic–to–articulatory mapping systembased on Gaussian mixture models, where it is shown they are suitable for improvingthe performance of such a systems over stop consonants. The method could be extended to other manner of articulation categories, e.g. fricatives,in order to adapt present method to acoustic-to-articulatory mapping systemsover whole speech.
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Ishimoto, Yuichi, and Noriko Suzuki. "Acoustic features of speech after glossectomy." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 3350–51. http://dx.doi.org/10.1121/1.4781416.

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Shuiskaya, Tatiana V., and Svetlana V. Androsova. "ACOUSTIC FEATURES OF CHILD SPEECH SOUNDS: CONSONANTS." Theoretical and Applied Linguistics 2, no. 3 (2016): 123–37. http://dx.doi.org/10.22250/2410-7190_2016_2_3_123_137.

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Kobayashi, Maori, Yasuhiro Hamada, and Masato Akagi. "Acoustic features in speech for emergency perception." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1835. http://dx.doi.org/10.1121/1.5068086.

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Roh, Yong-Wan, Dong-Ju Kim, Woo-Seok Lee, and Kwang-Seok Hong. "Novel acoustic features for speech emotion recognition." Science in China Series E: Technological Sciences 52, no. 7 (June 9, 2009): 1838–48. http://dx.doi.org/10.1007/s11431-009-0204-3.

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Yamamoto, Katsuhiko, Toshio Irino, Toshie Matsui, Shoko Araki, Keisuke Kinoshita, and Tomohiro Nakatani. "Analysis of acoustic features for speech intelligibility prediction models analysis of acoustic features for speech intelligibility prediction models." Journal of the Acoustical Society of America 140, no. 4 (October 2016): 3114. http://dx.doi.org/10.1121/1.4969744.

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Jiang, Wei, Zheng Wang, Jesse S. Jin, Xianfeng Han, and Chunguang Li. "Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network." Sensors 19, no. 12 (June 18, 2019): 2730. http://dx.doi.org/10.3390/s19122730.

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Automatic speech emotion recognition is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. We propose a novel deep neural architecture to extract the informative feature representations from the heterogeneous acoustic feature groups which may contain redundant and unrelated information leading to low emotion recognition performance in this work. After obtaining the informative features, a fusion network is trained to jointly learn the discriminative acoustic feature representation and a Support Vector Machine (SVM) is used as the final classifier for recognition task. Experimental results on the IEMOCAP dataset demonstrate that the proposed architecture improved the recognition performance, achieving accuracy of 64% compared to existing state-of-the-art approaches.
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Zlokarnik, Igor. "Adding articulatory features to acoustic features for automatic speech recognition." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 3246. http://dx.doi.org/10.1121/1.411699.

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Dissertations / Theses on the topic "Acoustic speech features"

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Leung, Ka Yee. "Combining acoustic features and articulatory features for speech recognition /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20LEUNGK.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 92-96). Also available in electronic version. Access restricted to campus users.
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Juneja, Amit. "Speech recognition based on phonetic features and acoustic landmarks." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2148.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.
Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Tyson, Na'im R. "Exploration of Acoustic Features for Automatic Vowel Discrimination in Spontaneous Speech." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339695879.

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Sun, Rui. "The evaluation of the stability of acoustic features in affective conveyance across multiple emotional databases." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49041.

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The objective of the research presented in this thesis was to systematically investigate the computational structure for cross-database emotion recognition. The research consisted of evaluating the stability of acoustic features, particularly the glottal and Teager Energy based features, and investigating three normalization methods and two data fusion techniques. One of the challenges of cross-database training and testing is accounting for the potential variation in the types of emotions expressed as well as the recording conditions. In an attempt to alleviate the impact of these types of variations, three normalization methods on the acoustic data were studied. Motivated by the lack of large and diverse enough emotional database to train the classifier, using multiple databases to train posed another challenge: data fusion. This thesis proposed two data fusion techniques, pre-classification SDS and post-classification ROVER to study the issue. Using the glottal, TEO and TECC features, of which the stability of emotion distinguishing ability has been highlighted on multiple databases, the systematic computational structure proposed in this thesis could improve the performance of cross-database binary-emotion recognition by up to 23% for neutral vs. emotional and 10% for positive vs. negative.
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Torres, Juan Félix. "Estimation of glottal source features from the spectral envelope of the acoustic speech signal." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34736.

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Speech communication encompasses diverse types of information, including phonetics, affective state, voice quality, and speaker identity. From a speech production standpoint, the acoustic speech signal can be mainly divided into glottal source and vocal tract components, which play distinct roles in rendering the various types of information it contains. Most deployed speech analysis systems, however, do not explicitly represent these two components as distinct entities, as their joint estimation from the acoustic speech signal becomes an ill-defined blind deconvolution problem. Nevertheless, because of the desire to understand glottal behavior and how it relates to perceived voice quality, there has been continued interest in explicitly estimating the glottal component of the speech signal. To this end, several inverse filtering (IF) algorithms have been proposed, but they are unreliable in practice because of the blind formulation of the separation problem. In an effort to develop a method that can bypass the challenging IF process, this thesis proposes a new glottal source information extraction method that relies on supervised machine learning to transform smoothed spectral representations of speech, which are already used in some of the most widely deployed and successful speech analysis applications, into a set of glottal source features. A transformation method based on Gaussian mixture regression (GMR) is presented and compared to current IF methods in terms of feature similarity, reliability, and speaker discrimination capability on a large speech corpus, and potential representations of the spectral envelope of speech are investigated for their ability represent glottal source variation in a predictable manner. The proposed system was found to produce glottal source features that reasonably matched their IF counterparts in many cases, while being less susceptible to spurious errors. The development of the proposed method entailed a study into the aspects of glottal source information that are already contained within the spectral features commonly used in speech analysis, yielding an objective assessment regarding the expected advantages of explicitly using glottal information extracted from the speech signal via currently available IF methods, versus the alternative of relying on the glottal source information that is implicitly contained in spectral envelope representations.
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Ishizuka, Kentaro. "Studies on Acoustic Features for Automatic Speech Recognition and Speaker Diarization in Real Environments." 京都大学 (Kyoto University), 2009. http://hdl.handle.net/2433/123834.

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Diekema, Emily D. "Acoustic Measurements of Clear Speech Cue Fade in Adults with Idiopathic Parkinson Disease." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460063159.

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Tran, Thi-Anh-Xuan. "Acoustic gesture modeling. Application to a Vietnamese speech recognition system." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT023/document.

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La sélection de caractéristiques acoustiques appropriées est essentielle dans tout système de traitement de la parole. Pendant près de 40 ans, la parole a été généralement considérée comme une séquence de signaux quasi-stables (voyelles) séparés par des transitions (consonnes). Bien qu‟un grand nombre d'études documentent clairement l'importance de la coarticulation, et révèlent que les cibles articulatoires et acoustiques ne sont pas indépendantes du contexte, l‟hypothèse que chaque voyelle présente une cible acoustique qui peut être spécifiée d'une manière indépendante du contexte reste très répandue. Ce point de vue implique des limitations fortes. Il est bien connu que les fréquences de formants sont des caractéristiques acoustiques qui présentent un lien évident avec la production de la parole, et qui peuvent participer à la distinction perceptive entre les voyelles. Par conséquent, les voyelles sont généralement décrites avec des configurations articulatoires statiques représentées par des cibles dans l'espace acoustique, généralement par les fréquences des formants correspondants, représentées dans les plans F1-F2 et F2-F3. Les consonnes occlusives peuvent être décrites en termes de point d'articulation, représentés par locus (ou locus équations) dans le plan acoustique. Mais les trajectoires des fréquences de formants dans la parole fluide présentent rarement un état d'équilibre pour chaque voyelle. Elles varient avec le locuteur, l'environnement consonantique (co-articulation) et le débit de parole (relative à un continuum entre hypo et hyper-articulation). En vue des limites inhérentes aux approches statiques, la démarche adoptée ici consiste à étudier les transitions entre les voyelles et les consonnes (V1V2 et V1CV2) d‟un point de vue dynamique
Speech plays a vital role in human communication. Selection of relevant acoustic speech features is key to in the design of any system using speech processing. For some 40 years, speech was typically considered as a sequence of quasi-stable portions of signal (vowels) separated by transitions (consonants). Despite a wealth of studies that clearly document the importance of coarticulation, and reveal that articulatory and acoustic targets are not context-independent, the view that each vowel has an acoustic target that can be specified in a context-independent manner remains widespread. This point of view entails strong limitations. It is well known that formant frequencies are acoustic characteristics that bear a clear relationship with speech production, and that can distinguish among vowels. Therefore, vowels are generally described with static articulatory configurations represented by targets in the acoustic space, typically by formant frequencies in F1-F2 and F2-F3 planes. Plosive consonants can be described in terms of places of articulation, represented by locus or locus equations in an acoustic plane. But formant frequencies trajectories in fluent speech rarely display a steady state for each vowel. They vary with speaker, consonantal environment (co-articulation) and speaking rate (relating to continuum between hypo- and hyper-articulation). In view of inherent limitations of static approaches, the approach adopted here consists in studying both vowels and consonants from a dynamic point of view.Firstly we studied the effects of the impulse response at the beginning, at the end and during transitions of the signal both in the speech signal and at the perception level. Variations of the phases of the components were then examined. Results show that the effects of these parameters can be observed in spectrograms. Crucially, the amplitudes of the spectral components distinguished under the approach advocated here are sufficient for perceptual discrimination. From this result, for all speech analysis, we only focus on amplitude domain, deliberately leaving aside phase information. Next we extent the work to vowel-consonant-vowel perception from a dynamic point of view. These perceptual results, together with those obtained earlier by Carré (2009a), show that vowel-to-vowel and vowel-consonant-vowel stimuli can be characterized and separated by the direction and rate of the transitions on formant plane, even when absolute frequency values are outside the vowel triangle (i.e. the vowel acoustic space in absolute values).Due to limitations of formant measurements, the dynamic approach needs to develop new tools, based on parameters that can replace formant frequency estimation. Spectral Subband Centroid Frequency (SSCF) features was studied. Comparison with vowel formant frequencies show that SSCFs can replace formant frequencies and act as “pseudo-formant” even during consonant production.On this basis, SSCF is used as a tool to compute dynamic characteristics. We propose a new way to model the dynamic speech features: we called it SSCF Angles. Our analysis work on SSCF Angles were performed on transitions of vowel-to-vowel (V1V2) sequences of both Vietnamese and French. SSCF Angles appear as reliable and robust parameters. For each language, the analysis results show that: (i) SSCF Angles can distinguish V1V2 transitions; (ii) V1V2 and V2V1 have symmetrical properties on the acoustic domain based on SSCF Angles; (iii) SSCF Angles for male and female are fairly similar in the same studied transition of context V1V2; and (iv) they are also more or less invariant for speech rate (normal speech rate and fast one). And finally, these dynamic acoustic speech features are used in Vietnamese automatic speech recognition system with several obtained interesting results
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Wang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.

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Chen, Jitong. "On Generalization of Supervised Speech Separation." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492038295603502.

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Books on the topic "Acoustic speech features"

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Gabsdil, Malte. Automatic classification of speech recognition hypotheses using acoustic and pragmatic features. Saarbrücken: DFKI & Universität des Saarlandes, 2005.

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Kálmán, Bolla. A phonetic conspectus of Polish: The articulatory and acoustic features of Polish speech sounds. Budapest: Linguistics Institute of the Hungarian Academy of Sciences, 1987.

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Kálmán, Béla. A Phonetic conspectus of English: The articulatory and acoustic features of British English speech sounds. Budapest: Linguistics Institute of the Hungarian Academy of Sciences, 1989.

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Bolla, Kálmán. A phonetic conspectus of English: The articulatory and acoustic features of British English speech sounds. Budapest: Linguistics Institute of the Hungarian Academy of Sciences, 1989.

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Kubozono, Haruo, ed. The Phonetics and Phonology of Geminate Consonants. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198754930.001.0001.

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Geminate consonants, also known as long consonants, appear in many languages in the world, and how they contrast with their short counterparts, or singletons (e.g. /tt/ vs. /t/), is an important topic that features in most linguistics and phonology textbooks. However, neither their phonetic manifestation nor their phonological nature is fully understood, much less their cross-linguistic similarities and differences. As the first volume specifically devoted to the phonetics and phonology of geminate consonants, this book aims to bring together novel, original data and analyses concerning many individual languages in different parts of the world, to present a wide range of perspectives for the study of phonological contrasts in general by introducing various experimental (acoustic, perceptual, physiological, and electrophysiological) and non-experimental methodologies, and to discuss phonological contrasts in a wider context than is generally considered by looking also at the behaviour of geminate consonants in loanword phonology and language acquisition. Studying geminate consonants requires interdisciplinary approaches including experimental phonetics (acoustics and speech perception), theoretical phonology, speech processing, neurolinguistics, and language acquisition. Providing phonetic and phonological details about geminate consonants across languages will greatly contribute to research in these fields.
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Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, and acoustic and language models, which are estimated in a training phase, and the decoder. The focus of this article is on methods used in state-of-the-art speaker-independent, large-vocabulary continuous speech recognition (LVCSR). Primary application areas for such technology are dictation, spoken language dialogue, and transcription for information archival and retrieval systems. Finally, this article discusses issues and directions of future research.
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Stanford, James N. New England English. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190625658.001.0001.

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For nearly 400 years, New England has held an important place in the development of American English, and “New England accents” are very well known in popular imagination. But since the 1930s, no large-scale academic book project has focused specifically on New England English. While other research projects have studied dialect features in various regions of New England, this is the first large-scale scholarly project to focus solely on New England English since the Linguistic Atlas of New England. This book presents new research covering all six New England states, with detailed geographic, phonetic, and statistical analysis of data collected from over 1,600 New Englanders. The book covers the past, present, and future of New England dialect features, analyzing them with dialect maps and statistical modeling in terms of age, gender, social class, ethnicity, and other factors. The book reports on a recent large-scale data collection project that included 367 field interviews, 626 audio-recorded interviews, and 634 online written questionnaires. Using computational methods, the project processed over 200,000 individual vowels in audio recordings to examine changes in New England speech. The researchers also manually examined 30,000 instances of /r/ to investigate “r-dropping” in words like “park” and so on. The book also reviews other recent research in the area. Using acoustic phonetics, computational processing, detailed statistical analyses, dialect maps, and graphical illustrations, the book systematically documents all of the major traditional New England dialect features, other regional features, and their current usage across New England.
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Book chapters on the topic "Acoustic speech features"

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Mizera, Petr, and Petr Pollak. "Improved Estimation of Articulatory Features Based on Acoustic Features with Temporal Context." In Text, Speech, and Dialogue, 560–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24033-6_63.

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Žibert, Janez, and France Mihelič. "Fusion of Acoustic and Prosodic Features for Speaker Clustering." In Text, Speech and Dialogue, 210–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04208-9_31.

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Lyakso, Elena, Olga Frolova, and Aleksey Grigorev. "Perception and Acoustic Features of Speech of Children with Autism Spectrum Disorders." In Speech and Computer, 602–12. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66429-3_60.

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Tomashenko, Natalia, Yuri Khokhlov, Anthony Larcher, and Yannick Estève. "Exploring GMM-derived Features for Unsupervised Adaptation of Deep Neural Network Acoustic Models." In Speech and Computer, 304–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_36.

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Kocharov, Daniil, Tatiana Kachkovskaia, Aliya Mirzagitova, and Pavel Skrelin. "Combining Syntactic and Acoustic Features for Prosodic Boundary Detection in Russian." In Statistical Language and Speech Processing, 68–79. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45925-7_6.

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Proença, Jorge, Arlindo Veiga, Sara Candeias, João Lemos, Cristina Januário, and Fernando Perdigão. "Characterizing Parkinson’s Disease Speech by Acoustic and Phonetic Features." In Lecture Notes in Computer Science, 24–35. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09761-9_3.

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Yasmin, Ghazaala, and Asit K. Das. "Speech and Non-speech Audio Files Discrimination Extracting Textural and Acoustic Features." In Recent Trends in Signal and Image Processing, 197–206. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8863-6_20.

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Verkhodanova, Vasilisa, and Vladimir Shapranov. "Filled Pauses and Lengthenings Detection Based on the Acoustic Features for the Spontaneous Russian Speech." In Speech and Computer, 227–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_28.

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Pao, Tsang-Long, Yu-Te Chen, Jun-Heng Yeh, and Wen-Yuan Liao. "Combining Acoustic Features for Improved Emotion Recognition in Mandarin Speech." In Affective Computing and Intelligent Interaction, 279–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573548_36.

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Lyakso, Elena, Olga Frolova, and Aleksey Grigorev. "A Comparison of Acoustic Features of Speech of Typically Developing Children and Children with Autism Spectrum Disorders." In Speech and Computer, 43–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_4.

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Conference papers on the topic "Acoustic speech features"

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Chen, Shizhe, Qin Jin, Xirong Li, Gang Yang, and Jieping Xu. "Speech emotion classification using acoustic features." In 2014 9th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2014. http://dx.doi.org/10.1109/iscslp.2014.6936664.

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Muroi, Takashi, Ryoichi Takashima, Tetsuya Takiguchi, and Yasuo Ariki. "Gradient-based acoustic features for speech recognition." In 2009 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS 2009). IEEE, 2009. http://dx.doi.org/10.1109/ispacs.2009.5383805.

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Li, Lujun, Chuxiong Qin, and Dan Qu. "Improvements of Acoustic Features for Speech Separation." In 2016 Joint International Information Technology, Mechanical and Electronic Engineering Conference. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/jimec-16.2016.23.

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Kocharov, Daniil, András Zolnay, Ralf Schlüter, and Hermann Ney. "Articulatory motivated acoustic features for speech recognition." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-122.

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Dominguez, Mónica, Mireia Farrús, and Leo Wanner. "Combining acoustic and linguistic features in phrase-oriented prosody prediction." In Speech Prosody 2016. ISCA, 2016. http://dx.doi.org/10.21437/speechprosody.2016-163.

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Jianfang Tang and Haiyan Zhang. "Acoustic features comparison from Chinese speech emotion recognition." In 2012 IEEE International Conference on Oxide Materials for Electronic Engineering (OMEE). IEEE, 2012. http://dx.doi.org/10.1109/omee.2012.6343661.

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Ramdinmawii, Esther, and Vinay Kumar Mittal. "Emotional speech discrimination using sub-segmental acoustic features." In 2017 2nd International Conference on Telecommunication and Networks (TEL-NET). IEEE, 2017. http://dx.doi.org/10.1109/tel-net.2017.8343515.

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Das, Rohan Kumar, Jichen Yang, and Haizhou Li. "Long Range Acoustic Features for Spoofed Speech Detection." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1887.

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Jin, Qin, Chengxin Li, Shizhe Chen, and Huimin Wu. "Speech emotion recognition with acoustic and lexical features." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178872.

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Harding, Philip, and Ben Milner. "Speech enhancement by reconstruction from cleaned acoustic features." In Interspeech 2011. ISCA: ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-420.

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