Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition.Comment: 31 page

A Comparison of Front-Ends for Bitstream-Based ASR over IP

Automatic speech recognition (ASR) is called to play a relevant role in the provision of spoken interfaces for IP-based applications. However, as a consequence of the transit of the speech signal over these particular networks, ASR systems need to face two new challenges: the impoverishment of the speech quality due to the compression needed to fit the channel capacity and the inevitable occurrence of packet losses. In this framework, bitstream-based approaches that obtain the ASR feature vectors directly from the coded bitstream, avoiding the speech decoding process, have been proposed ([S.H. Choi, H.K. Kim, H.S. Lee, Speech recognition using quantized LSP parameters and their transformations in digital communications, Speech Commun. 30 (4) (2000) 223–233. A. Gallardo-Antolín, C. Pelàez-Moreno, F. Díaz-de-María, Recognizing GSM digital speech, IEEE Trans. Speech Audio Process., to appear. H.K. Kim, R.V. Cox, R.C. Rose, Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments, IEEE Trans. Speech Audio Process. 10 (8) (2002) 591–604. C. Peláez-Moreno, A. Gallardo-Antolín, F. Díaz-de-María, Recognizing voice over IP networks: a robust front-end for speech recognition on the WWW, IEEE Trans. Multimedia 3(2) (2001) 209–218], among others) to improve the robustness of ASR systems. LSP (Line Spectral Pairs) are the preferred set of parameters for the description of the speech spectral envelope in most of the modern speech coders. Nevertheless, LSP have proved to be unsuitable for ASR, and they must be transformed into cepstrum-type parameters. In this paper we comparatively evaluate the robustness of the most significant LSP to cepstrum transformations in a simulated VoIP (voice over IP) environment which includes two of the most popular codecs used in that network (G.723.1 and G.729) and several network conditions. In particular, we compare ‘pseudocepstrum’ [H.K. Kim, S.H. Choi, H.S. Lee, On approximating Line Spectral Frequencies to LPC cepstral coefficients, IEEE Trans. Speech Audio Process. 8 (2) (2000) 195–199], an approximated but straightforward transformation of LSP into LP cepstral coefficients, with a more computationally demanding but exact one. Our results show that pseudocepstrum is preferable when network conditions are good or computational resources low, while the exact procedure is recommended when network conditions become more adverse.Publicad

Enhanced amplitude modulations contribute to the Lombard intelligibility benefit: Evidence from the Nijmegen Corpus of Lombard Speech

Speakers adjust their voice when talking in noise, which is known as Lombard speech. These acoustic adjustments facilitate speech comprehension in noise relative to plain speech (i.e., speech produced in quiet). However, exactly which characteristics of Lombard speech drive this intelligibility benefit in noise remains unclear. This study assessed the contribution of enhanced amplitude modulations to the Lombard speech intelligibility benefit by demonstrating that (1) native speakers of Dutch in the Nijmegen Corpus of Lombard Speech (NiCLS) produce more pronounced amplitude modulations in noise vs. in quiet; (2) more enhanced amplitude modulations correlate positively with intelligibility in a speech-in-noise perception experiment; (3) transplanting the amplitude modulations from Lombard speech onto plain speech leads to an intelligibility improvement, suggesting that enhanced amplitude modulations in Lombard speech contribute towards intelligibility in noise. Results are discussed in light of recent neurobiological models of speech perception with reference to neural oscillators phase-locking to the amplitude modulations in speech, guiding the processing of speech

A Subband-Based SVM Front-End for Robust ASR

This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels