Anti-spoofing Methods for Automatic SpeakerVerification System

Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them. Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still vulnerable to spoofing attacks. Inthis work we overview different acoustic feature spaces and classifiersto determine reliable and robust countermeasures against spoofing at-tacks. We compared several spoofing detection systems, presented so far,on the development and evaluation datasets of the Automatic SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge 2015.Experimental results presented in this paper demonstrate that the useof magnitude and phase information combination provides a substantialinput into the efficiency of the spoofing detection systems. Also wavelet-based features show impressive results in terms of equal error rate. Inour overview we compare spoofing performance for systems based on dif-ferent classifiers. Comparison results demonstrate that the linear SVMclassifier outperforms the conventional GMM approach. However, manyresearchers inspired by the great success of deep neural networks (DNN)approaches in the automatic speech recognition, applied DNN in thespoofing detection task and obtained quite low EER for known and un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer and Information Science (CCIS) vol. 66

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Percepcijska utemeljenost kepstranih mjera udaljenosti za primjene u obradi govora

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are determined by several parameters used in mel cepstral analysis. The aim of this work is to examine compatibility of MFCC measure with human perception for different values of parameters in the analysis. By analysing mel filter bank parameters it is found that filter bank with 24 bands, 220 mels bandwidth and band overlap coefficient equal and higher than one gives optimal spectral distortion (SD) distance measures. For this kind of mel filter bank, the difference between vowels can be recognised for full-length mel cepstral SD RMS measure higher than 0.4 - 0.5 dB. Further on, we will show that usage of truncated mel cepstral vector (12 coefficients) is justified for speech recognition, but may be arguable for speaker recognition. We also analysed the impact of aliasing in cepstral domain on cepstral distortion measures. The results showed high correlation of SD distances calculated from aperiodic and periodic mel cepstrum, leading to the conclusion that the impact of aliasing is generally minor. There are rare exceptions where aliasing is present, and these were also analysed.Jedna od danas najčešće korištenih mjera u automatskom prepoznavanju govora i govornika je mjera euklidske udaljenosti MFCC vektora. Algoritam za izračunavanje mel frekvencijskih kepstralnih koeficijenata zasniva se na filtarskom slogu kod kojeg su pojasi ekvidistantno raspoređeni na percepcijski motiviranoj mel skali. Na vrijednost mel kepstralnog vektora, a samim time i na svojstva kepstralne mjere udaljenosti glasova, utječe veći broj parametara sustava za kepstralnu analizu. Tema ovog rada je ispitati usklađenost MFCC mjere sa stvarnim percepcijskim razlikama za različite vrijednosti parametara analize. Analizom parametara mel filtarskog sloga utvrdili smo da filtar sa 24 pojasa, širine 220 mel-a i faktorom preklapanja filtra većim ili jednakim jedan, daje optimalne SD mjere koje se najbolje slažu s percepcijom. Za takav mel filtarski slog granica čujnosti razlike između glasova je 0.4-0.5 dB, mjereno SD RMS razlikom potpunih mel kepstralnih vektora. Također, pokazat ćemo da je korištenje mel kepstralnog vektora odrezanog na konačnu dužinu (12 koeficijenata) opravdano za prepoznavanje govora, ali da bi moglo biti upitno u primjenama prepoznavanja govornika. Analizirali smo i utjecaj preklapanja spektara u kepstralnoj domeni na mjere udaljenosti glasova. Utvrđena je izrazita koreliranost SD razlika izračunatih iz aperiodskog i periodičkog mel kepstra iz čega zaključujemo da je utjecaj preklapanja spektara generalno zanemariv. Postoje rijetke iznimke kod kojih je utjecaj preklapanja spektara prisutan, te su one posebno analizirane

Modeling Sub-Band Information Through Discrete Wavelet Transform to Improve Intelligibility Assessment of Dysarthric Speech

The speech signal within a sub-band varies at a fine level depending on the type, and level of dysarthria. The Mel-frequency filterbank used in the computation process of cepstral coefficients smoothed out this fine level information in the higher frequency regions due to the larger bandwidth of filters. To capture the sub-band information, in this paper, four-level discrete wavelet transform (DWT) decomposition is firstly performed to decompose the input speech signal into approximation and detail coefficients, respectively, at each level. For a particular input speech signal, five speech signals representing different sub-bands are then reconstructed using inverse DWT (IDWT). The log filterbank energies are computed by analyzing the short-term discrete Fourier transform magnitude spectra of each reconstructed speech using a 30-channel Mel-filterbank. For each analysis frame, the log filterbank energies obtained across all reconstructed speech signals are pooled together, and discrete cosine transform is performed to represent the cepstral feature, here termed as discrete wavelet transform reconstructed (DWTR)- Mel frequency cepstral coefficient (MFCC). The i-vector based dysarthric level assessment system developed on the universal access speech corpus shows that the proposed DTWRMFCC feature outperforms the conventional MFCC and several other cepstral features reported for a similar task. The usages of DWTR- MFCC improve the detection accuracy rate (DAR) of the dysarthric level assessment system in the text and the speaker-independent test case to 60.094 % from 56.646 % MFCC baseline. Further analysis of the confusion matrices shows that confusion among different dysarthric classes is quite different for MFCC and DWTR-MFCC features. Motivated by this observation, a two-stage classification approach employing discriminating power of both kinds of features is proposed to improve the overall performance of the developed dysarthric level assessment system. The two-stage classification scheme further improves the DAR to 65.813 % in the text and speaker- independent test case

Listening to features

This work explores nonparametric methods which aim at synthesizing audio from low-dimensionnal acoustic features typically used in MIR frameworks. Several issues prevent this task to be straightforwardly achieved. Such features are designed for analysis and not for synthesis, thus favoring high-level description over easily inverted acoustic representation. Whereas some previous studies already considered the problem of synthesizing audio from features such as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit formula used to compute those features in order to inverse them. Here, we instead adopt a simple blind approach, where arbitrary sets of features can be used during synthesis and where reconstruction is exemplar-based. After testing the approach on a speech synthesis from well known features problem, we apply it to the more complex task of inverting songs from the Million Song Dataset. What makes this task harder is twofold. First, that features are irregularly spaced in the temporal domain according to an onset-based segmentation. Second the exact method used to compute these features is unknown, although the features for new audio can be computed using their API as a black-box. In this paper, we detail these difficulties and present a framework to nonetheless attempting such synthesis by concatenating audio samples from a training dataset, whose features have been computed beforehand. Samples are selected at the segment level, in the feature space with a simple nearest neighbor search. Additionnal constraints can then be defined to enhance the synthesis pertinence. Preliminary experiments are presented using RWC and GTZAN audio datasets to synthesize tracks from the Million Song Dataset.Comment: Technical Repor