9,903 research outputs found
Recognizing Voice Over IP: A Robust Front-End for Speech Recognition on the World Wide Web
The Internet Protocol (IP) environment poses two relevant sources of distortion to the speech recognition problem: lossy speech coding and packet loss. In this paper, we propose a new front-end for speech recognition over IP networks. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bit stream) instead of decoding it and subsequently extracting the feature vectors. This approach offers two significant benefits. First, the recognition system is only affected by the quantization distortion of the spectral envelope. Thus, we are avoiding the influence of other sources of distortion due to the encoding-decoding process. Second, when packet loss occurs, our front-end becomes more effective since it is not constrained to the error handling mechanism of the codec. We have considered the ITU G.723.1 standard codec, which is one of the most preponderant coding algorithms in voice over IP (VoIP) and compared the proposed front-end with the conventional approach in two automatic speech recognition (ASR) tasks, namely, speaker-independent isolated digit recognition and speaker-independent continuous speech recognition. In general, our approach outperforms the conventional procedure, for a variety of simulated packet loss rates. Furthermore, the improvement is higher as network conditions worsen.Publicad
Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010
Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use
A Hybrid Parameterization Technique for Speaker Identification
Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. The separation methodology is based in Joint Process Estimation under the un-correlation hypothesis between vocal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency domains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speakers are shown comparing different parameterization strategies. The results confirm the better performance of decoupled parameterization compared against approaches based on plain speech parameterization
A modulation property of time-frequency derivatives of filtered phase and its application to aperiodicity and fo estimation
We introduce a simple and linear SNR (strictly speaking, periodic to random
power ratio) estimator (0dB to 80dB without additional
calibration/linearization) for providing reliable descriptions of aperiodicity
in speech corpus. The main idea of this method is to estimate the background
random noise level without directly extracting the background noise. The
proposed method is applicable to a wide variety of time windowing functions
with very low sidelobe levels. The estimate combines the frequency derivative
and the time-frequency derivative of the mapping from filter center frequency
to the output instantaneous frequency. This procedure can replace the
periodicity detection and aperiodicity estimation subsystems of recently
introduced open source vocoder, YANG vocoder. Source code of MATLAB
implementation of this method will also be open sourced.Comment: 8 pages 9 figures, Submitted and accepted in Interspeech201
Reconstructing intelligible audio speech from visual speech features
This work describes an investigation into the feasibility of producing intelligible audio speech from only visual speech fea- tures. The proposed method aims to estimate a spectral enve- lope from visual features which is then combined with an arti- ficial excitation signal and used within a model of speech pro- duction to reconstruct an audio signal. Different combinations of audio and visual features are considered, along with both a statistical method of estimation and a deep neural network. The intelligibility of the reconstructed audio speech is measured by human listeners, and then compared to the intelligibility of the video signal only and when combined with the reconstructed audio
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