José L. Pérez-Córdoba

Histogram equalization of speech representation for robust speech recognition

This paper describes a method of compensating for nonlinear distortions in speech representation caused by noise. The method described here is based on the histogram equalization method often used in digital image processing. Histogram equalization is applied to each component of the feature vector in order to improve the robustness of speech recognition systems. The paper describes how the proposed method can be applied to robust speech recognition and it is compared with other compensation techniques. The recognition experiments, including results in the AURORA II framework, demonstrate the effectiveness of histogram equalization when it is applied either alone or in combination with other compensation techniques.

HMM-based channel error mitigation and its application to distributed speech recognition☆

The emergence of distributed speech recognition has generated the need to mitigate the degradations that the transmission channel introduces in the speech features used for recognition. This work proposes a hidden Markov model (HMM) framework from which different mitigation techniques oriented to wireless channels can be derived. First, we study the performance of two techniques based on the use of a minimum mean square error (MMSE) esti- mation, a raw MMSE and a forward MMSE estimation, over additive white Gaussian noise (AWGN) channels. These techniques are also adapted to bursty channels. Then, we propose two new mitigation methods specially suitable for bursty channels. The first one is based on a forward-backward MMSE estimation and the second one on the well- known Viterbi algorithm. Different experiments are carried out, dealing with several issues such as the application of hard decisions on the received bits or the influence of the estimated channel SNR. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions. 2003 Elsevier B.V. All rights reserved.

Efficient MMSE-based channel error mitigation techniques. Application to distributed speech recognition over wireless channels

This work addresses the mitigation of channel errors by means of efficient minimum mean-square-error (MMSE) estimation. Although powerful model-based implementations have been recently proposed, the computational burden involved can make them impractical. We propose two new approaches that maintain a good level of performance with a low computational complexity. These approaches keep the simple structure and complexity of a raw MMSE estimation, although they enhance it with additional source a priori knowledge. The proposed techniques are built on a distributed speech recognition system. Different degrees of tradeoff between recognition performance and computational complexity are obtained.

Packet loss concealment based on VQ replicas and MMSE estimation applied to distributed speech recognition

This paper proposes a new packet loss concealment technique based on the inclusion in each packet of a few FEC bits, representing data replicas, combined with a minimum mean square error estimation (MMSE). This technique is developed for an Aurora-2 distributed speech recognition system working over an IP network. In addition to the data representing the transmitted speech frames, each packet includes some FEC bits representing a strongly VQ-quantized version (replicas) of previous and subsequent frames. When a loss burst occurs, the lost frames can be reconstructed from the VQ replicas. In order to mitigate the degradation introduced by the coarse VQ quantization of the replicas, a model-based MMSE estimation is applied. The experimental results show that, under a strongly degraded channel, it is possible to obtain up to 83.31 % of word accuracy with only 4 FEC bits or 88.47 % with 8 FEC bits per packet, when the Aurora mitigation algorithm only obtains 76.98 %.

A new signal characterization and signal-based Chou's PseAAC representation of protein sequences

Most of the algorithms used for information extraction and for processing the amino acid chains that make up proteins treat them as symbolic chains. Fewer algorithms exploit signal processing techniques that require a numerical representation of amino acid chains. However, these algorithms are very powerful for extracting regularities that cannot be detected when working with a symbolic chain, which may be important for understanding the biological meaning of a sequence or in classification tasks. In this study, a new mathematical representation of amino acid chains is proposed, which is derived using a similarity measure based on the PAM250 amino acid substitution matrix and that generates 20 signals for each protein sequence. Using this representation 20 consensus spectra for a protein family are determined and the relevance of the frequency peaks is established, obtaining a group of significant frequency peaks that manifest common periodicities of the amino acid sequences that belong to a protein family. We also show that the proposed representation in 20 signals can be integrated into Chous pseudo amino acid composition (PseAAC) and constitute a useful alternative to amino acid physicochemical properties in Chous PseAAC.

A scalable coding scheme based on interframe dependency limitation

While VoIP (voice over IP) is gaining importance in comparison with other types of telephony, packet loss remains as the main source of degradation in VoIP systems. Traditional speech codecs, such as those based on the CELP (code excited linear prediction) paradigm, can achieve low bit-rates at the cost of introducing interframe dependencies. As a result, the effect of a packet loss burst is propagated to the frames correctly received after the burst. iLBC (internet low bit-rate codec) alleviates this problem by removing the interframe dependencies at the cost of a higher bit-rate. In this paper we propose a combination of iLBC with an ACELP (algebraic CELP) codec in which a variable number of ACELP-coded frames is inserted between every two iLBC-coded frames. The experimental results show that the combined codec can achieve a performance close to that of iLBC at different loss conditions but with a smaller bit-rate. Also, scalability is achieved by modifying the number of inserted ACELP-coded frames.

MMSE-Based Packet Loss Concealment for CELP-Coded Speech Recognition

In this paper, we analyze the performance of network speech recognition (NSR) over IP networks, adapting and proposing new solutions to the packet loss problem for code excited linear prediction (CELP) codecs. NSR has a client-server architecture which places the recognizer at the server side using a standard speech codec for speech transmission. Its main advantage is that no changes are required for the existing client devices and networks. However, the use of speech codecs degrades its performance, mainly in the presence of packet losses. First, we study the degradations introduced by CELP codecs in lossy packet networks. Later, we propose a reconstruction technique based on minimum mean square error (MMSE) estimation using hidden Markov models. This approach also allows us to obtain reliability measures associated to each estimate. We show how to use this information to improve the recognition performance by means of soft-data decoding and weighted Viterbi algorithm. The experimental results are obtained for two well-known CELP codecs, G.729 and AMR 12.2 kbps, carrying out recognition from decoded speech. Finally, we analyze an efficient and improved implementation of the proposed techniques using an NSR system which extracts speech recognition features directly from the bit-stream parameters. The experimental results show that the different proposed NSR systems achieve a comparable performance to distributed speech recognition (DSR).

Low complexity channel error mitigation for distributed speech recognition over wireless channels

Distributed speech recognition (DSR) has been recently proposed as an efficient way of translating automatic speech recognition technologies to mobile and IP network application. In this paper we propose a channel error mitigation technique with a low computational complexity that improves the mitigation technique proposed in the ETSI standard for DSR (ETSI-ES-201-108 v1.12) for bad channel conditions. We also study the influence of the vector quantization index assignment on the proposed mitigation technique and design a new index assignment that gets some improvement on the proposed technique.

Channel optimized matrix quantization (COMQ) of LSP parameters over waveform channels

Combined source and channel coding is a technique to mitigate channel errors without increasing the bit error rate. Channel optimized vector quantizer (COVQ) performs these objectives in the context of vector quantization. This paper presents a study of channel optimized matrix quantizer (COMQ) applied to quantize the line spectral pair (LSP) parameters as an extension of COVQ technique. Gaussian and slow-fading Rayleigh channels are considered and GMSK (Gaussian minimum shift-keying) is used as modulation technique. Several channel signal to noise ratio (CSNR) are considered to measure the performance of this system. In addition, for comparison purposes, the performance of other schemes for quantizing the LSP parameters are computed.

Progressive image transmission over a noisy channel using wavelet transform and channel optimized vector quantization

This paper studies a progressive image transmission technique over waveform channels. The channel optimized vector quantization codec (COVQ) (Farvardin and Vaishampayan 1991) is applied to the image wavelet coefficients creating a robust progressive image transmission technique that mitigates the effects of a noisy channel on the reconstructed image. In order to evaluate the performance of our proposal, a Gaussian and slow-fading Rayleigh channel model, with several different values of channel signal to noise ratio (CSNR) were simulated in our experiments. Examples show a significant visual improvement of our application compared to other progressive image transmission techniques.