120 research outputs found
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-AntolÃn, C. Pelà ez-Moreno, F. DÃaz-de-MarÃa, 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-AntolÃn, F. DÃaz-de-MarÃa, 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
Evaluation of mfcc estimation techniques for music similarity
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200
Speaker recognition using frequency filtered spectral energies
The spectral parameters that result from filtering the
frequency sequence of log mel-scaled filter-bank energies
with a simple first or second order FIR filter have proved
to be an efficient speech representation in terms of both
speech recognition rate and computational load. Recently,
the authors have shown that this frequency filtering can
approximately equalize the cepstrum variance enhancing
the oscillations of the spectral envelope curve that are
most effective for discrimination between speakers. Even
better speaker identification results than using melcepstrum
have been obtained on the TIMIT database,
especially when white noise was added. On the other
hand, the hybridization of both linear prediction and
filter-bank spectral analysis using either cepstral
transformation or the alternative frequency filtering has
been explored for speaker verification. The combination
of hybrid spectral analysis and frequency filtering, that
had shown to be able to outperform the conventional
techniques in clean and noisy word recognition, has yield
good text-dependent speaker verification results on the
new speaker-oriented telephone-line POLYCOST
database.Peer ReviewedPostprint (published version
Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding
Most current very low bit rate (VLBR) speech coding systems use hidden Markov
model (HMM) based speech recognition/synthesis techniques. This allows
transmission of information (such as phonemes) segment by segment that
decreases the bit rate. However, the encoder based on a phoneme speech
recognition may create bursts of segmental errors. Segmental errors are further
propagated to optional suprasegmental (such as syllable) information coding.
Together with the errors of voicing detection in pitch parametrization,
HMM-based speech coding creates speech discontinuities and unnatural speech
sound artefacts.
In this paper, we propose a novel VLBR speech coding framework based on
neural networks (NNs) for end-to-end speech analysis and synthesis without
HMMs. The speech coding framework relies on phonological (sub-phonetic)
representation of speech, and it is designed as a composition of deep and
spiking NNs: a bank of phonological analysers at the transmitter, and a
phonological synthesizer at the receiver, both realised as deep NNs, and a
spiking NN as an incremental and robust encoder of syllable boundaries for
coding of continuous fundamental frequency (F0). A combination of phonological
features defines much more sound patterns than phonetic features defined by
HMM-based speech coders, and the finer analysis/synthesis code contributes into
smoother encoded speech. Listeners significantly prefer the NN-based approach
due to fewer discontinuities and speech artefacts of the encoded speech. A
single forward pass is required during the speech encoding and decoding. The
proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s
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