A tutorial on signal processing in state-of-the-art speech recognition systems is presented, reviewing those techniques most commonly used. The four basic operations of signal modeling, i.e. spectral shaping, spectral analysis, parametric transformation, and statistical modeling, are discussed. Three important trends that have developed in the last five years in speech recognition are examined. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similarity transform techniques, often used to normalize and decorrelate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal's spectrum can be estimated in a closed-loop manner. The signal processing components of these algorithms are reviewed. >

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Signal modeling techniques in speech recognition

Speech recognition in noisy environments: a survey

https://hal.inria.fr/inria-00075657/document

Distance measures for signal processing and pattern recognition

This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for speech input. Without such processing, mismatches between training and testing conditions produce an unacceptable degradation in recognition accuracy.
Two kinds of environmental variability are introduced by the use of desk-top microphones and different training and testing conditions: additive noise and spectral tilt introduced by linear filtering. An important attribute of the novel compensation algorithms described in this thesis is that they provide joint rather than independent compensation for these two types of degradation.
Acoustical compensation is applied in our algorithms as an additive correction in the cepstral domain. This allows a higher degree of integration within SPHINX, the Carnegie Mellon speech recognition system, that uses the cepstrum as its feature vector. Therefore, these algorithms can be implemented very efficiently. Processing in many of these algorithms is based on instantaneous signal-to-noise ratio (SNR), as the appropriate compensation represents a form of noise suppression at low SNRs and spectral equalization at high SNRs.
The compensation vectors for additive noise and spectral transformations are estimated by minimizing the differences between speech feature vectors obtained from a "standard" training corpus of speech and feature vectors that represent the current acoustical environment. In our work this is accomplished by minimizing the distortion of vector-quantized cepstra that are produced by the feature extraction module in SPHINX.
In this dissertation we describe several algorithms including the SNR-Dependent Cepstral Normalization, (SDCN) and the Codeword-Dependent Cepstral Normalization (CDCN). With CDCN, the accuracy of SPHINX when trained on speech recorded with a close-talking microphone and tested on speech recorded with a desk-top microphone is essentially the same obtained when the system is trained and tested on speech from the desk-top microphone.
An algorithm for frequency normalization has also been proposed in which the parameter of the bilinear transformation that is used by the signal-processing stage to produce frequency warping is adjusted for each new speaker and acoustical environment. The optimum value of this parameter is again chosen to minimize the vector-quantization distortion between the standard environment and the current one. In preliminary studies, use of this frequency normalization produced a moderate additional decrease in the observed error rate.

/pdf/acoustical-and-environmental-robustness-in-automatic-speech-3yf73aaxj5.pdf

Acoustical and environmental robustness in automatic speech recognition

The ASVspoof initiative was created to promote the development of countermeasures which aim to protect automatic speaker verification (ASV) from spoofing attacks. The first community-led, common evaluation held in 2015 focused on countermeasures for speech synthesis and voice conversion spoofing attacks. Arguably, however, it is replay attacks which pose the greatest threat. Such attacks involve the replay of recordings collected from enrolled speakers in order to provoke false alarms and can be mounted with greater ease using everyday consumer devices. ASVspoof 2017, the second in the series, hence focused on the development of replay attack countermeasures. This paper describes the database, protocols and initial findings. The evaluation entailed highly heterogeneous acoustic recording and replay conditions which increased the equal error rate (EER) of a baseline ASV system from 1.76% to 31.46%. Submissions were received from 49 research teams, 20 of which improved upon a baseline replay spoofing detector EER of 24.77%, in terms of replay/non-replay discrimination. While largely successful, the evaluation indicates that the quest for countermeasures which are resilient in the face of variable replay attacks remains very much alive.

The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection

Comparative study of several distortion measures for speech recognition

In this study we compared several different spectral distortion measures including the Itakura-Saito (IS), the log likelihood ratio (LLR), the likelihood ratio (LR), the cepstral (CEP), and two perceptually based distortion measures, the weighted likelihood ratio (WLR) and the weighted slope metric (WSM) distortion measures, in terms of their effects on the performance of a standard dynamic time warping (DTW) based, isolated word, speech recognizer. Two modifications of the basic forms of each measure were also investigated, namely a Bark-scale frequency warping and the incorporation of suprasegmental energy information. All distortion measures and their modifications were tested on an alpha-digit vocabulary, 4-talker, telephone recording data base. The results can be summarized as: (1) All LPC-based distortion measures performed reasonably well. The LLR and WSM distortion measures gave the highest recognition accuracy, while the IS distortion measure gave the lowest score; (2) Whereas the addition of suprasegmental energy information helped the recognition performance, the use of gain and absolute loudness degraded the performance; (3) Bark-scale frequency warping did not perform as well as its unwarped counterpart; (4) The WLR distortion measure did not perform as well as its unweighted counterpart.

Dennis H. Klatt

Papers

Comparative study of several distortion measures for speech recognition

Comparative study of several distortion measures for speech recognition