Digital processing of speech signals

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.

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Emotion recognition from speech: a review

Psychoacoustics—facts and models

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Advanced Digital Signal Processing And Noise Reduction

Clinical Measurement Of Speech And Voice

Impact of vocal effort variability on automatic speech recognition

This paper briefly describes a newly created database of Czech speech under realistic stressed conditions and presents some selected results achieved by analysing stressed speech. The motivation for creating a new database was the non-existence of stressed speech corpora for Czech or any other Slavic language. The database contains read and conversational speech both in neutral and stressed state of 31 male speakers. The stressed speech was recorded during final oral examinations at the Brno University of Technology. In order to quantify the stress of individual speakers the speaker's heart rate was also measured and recorded simultaneously with the speech. Experiments conducted using this database show that the speech corpus can be used for development and evaluation of specific algorithms by identifying the extent of stress speakers have by analyzing their voices only.

Introducing the Database ExamStress for Speech under Stress

Summary This paper presents and discusses an approach to automatic gender distinction in a short segment of normally spoken continuous speech. In order to see which phonemes are effective for gender recognition, we analyzed individual vowels. Two different simple identifiers based on selected mel-frequency cepstral coefficients were evaluated. Using vowel phonemes, we achieved in short-time analysis (20 msec) a gender identification accuracy of more than 90%. Especially for vowel “a“, almost no error occurs. For text-independent analysis, the speech duration of 500 msec was sufficient to identify male/female speakers with the accuracy of more than 93%. Automatic estimation of speaker’s gender by her/his voice is an important factor to realize high-quality dialogue systems.

/pdf/gender-distinction-using-short-segments-of-speech-signal-2a4h664z2e.pdf

Gender Distinction Using Short Segments of Speech Signal

Summary This paper deals with speech signal as significant indicator of psychological stress when the speaker is involved in a stressful activity. The investigation of speaker’s stress is based on specific changes in short-time spectrum of vowel phonemes. For each selected signal segment, the spectrum is computed by means of two different methods: Fourier transformation and chirp transformation. Comparative results between both spectra serve for speaker’s stress detection. In case of speech under stress, the obtained spectra differ towards the higher frequencies due to enhanced pitch modulation observed in the envelope of the chirp spectrum. For use in our experiments, a new database of speech under stress consisting of data collected during oral examinations at our university was created.

/pdf/spectral-analysis-of-speech-under-stress-2upibe5g1d.pdf

Spectral Analysis of Speech under Stress

This paper describes development of reliable gunshot detection system with emphasis on low power consumption for use in counter-poacher devices primarily protecting elephants in Africa. Intended system will work as a binary detector of gunfire without further classification of used fire-arm. Dominance of right gunshot detection over false alarms is crucial. Proposed recognition system is based on linear predictive coefficients, correlation against template and comparison of spectral energy in sub-bands.

Milan Sigmund

Papers

Impact of vocal effort variability on automatic speech recognition

Introducing the Database ExamStress for Speech under Stress

Gender Distinction Using Short Segments of Speech Signal

Spectral Analysis of Speech under Stress

Acoustical detection of gunshots