We present a back-end solution developed at Texas Instruments for noise robust speech recognition. The solution consists of three techniques: 1) a joint additive and convolutive noise compensation (JAC) which adapts speech acoustic models; 2) an enhanced channel estimation procedure which extends JAC performance towards lower SNR ranges; 3) an N-pass decoding algorithm. The performance of the proposed back-end is evaluated on the Aurora-2 database. With 20% fewer model parameters and without the need for the second order derivative of the recognition features, the performance of the proposed solution is 91.86%, which outperforms that of the ETSI advanced front-end standard (88.19%) by more than 30% relative word error rate reduction.

Training triphone acoustic models for speech recognition is time-consuming and requires important manual intervention. We present an alternative solution, performing automatic training by use of a pronunciation phonetic table which summarizes the articulatory characteristics of the target language. The method is able to train triphones for any language, given an existing set of reference monophones in one or more languages, by automatically performing the tasks of monophone seeding, triphone clustering and other training steps. The automatic nature of the training algorithm lends itself to parameter optimization, which can further improve recognition accuracy with respect to manually trained models. In a continuous digit recognition experiment, it is shown that automatically generated triphone models gave a 1.26% error rate, compared to a 2.30% error rate for its manual counterpart.

The performance of speech recognition systems trained in quiet degrades signicantly under noisy conditions. To address this problem, a Weighted Viterbi Recognition (WVR) algorithm that is a function of the SNR of each speech frame is proposed. Acoustic models trained on clean data, and the acoustic front-end features are kept unchanged in this approach. Instead, a condence/rob ustness factor is assigned to the output observation probability of each speech frame according to its SNR estimate during the Viterbi decoding stage. Comparative experiments are conducted with Weighted Viterbi Recognition with different front-end features such as MFCC, LPCC and PLP. Results show consistent improvements with all three feature vectors. For a reasonable size of adaptation data, WVR outperforms environment adaptation using MLLR.

In remote (or distributed) speech recognition , the recognition features are quantized at the client, and transmitted to the server via wireless or packet-based communication for recognition. In this paper, we investigate the issue of robustness of remote speech recognition applications against channel noise. The techniques presented include: 1) optimal soft decision channel decoding allowing for error detection, 2) weighted Viterbi recognition (WVR) with weighting coefficients based on the channel decoding reliability, 3) frame erasure concealment, and 4) WVR with weighting coefficients based on the quality of the erasure concealment operation. The techniques presented are implemented at the receiver (server), which limit the complexity for the client, and significantly extend the range of channel conditions for which remote recognition can be sustained. As a case study, we illustrate that remote recognition based on perceptual linear prediction (PLP) coefficients is able to provide at less than 500 bps, good recognition accuracy over a wide range of channel conditions.

We present a framework for developing source coding, channel coding and decoding as well as erasure concealment techniques adapted for distributed (wireless or packet-based) speech recognition. It is shown that speech recognition as opposed to speech coding, is more sensitive to channel errors than channel erasures, and appropriate channel coding design criteria are determined. For channel decoding, we introduce a novel technique for combining at the receiver soft decision decoding with error detection. Frame erasure concealment techniques are used at the decoder to deal with unreliable frames. At the recognition stage, we present a technique to modify the recognition engine itself to take into account the time-varying reliability of the decoded feature after channel transmission. The resulting engine, referred to as weighted Viterbi recognition, further improves the recognition accuracy. Together, source coding, channel coding and the modified recognition engine are shown to provide good recognition accuracy over a wide range of communication channels with bit rates of 1.2 kbps or less.

This paper presents bandwidth-efficient speech transmission systems using rate-compatible channel coders and variable bitrate embedded source coders. Rate-compatible punctured convolutional codes (RCPC) are often used to provide unequal error protection (UEP) via progressive bit puncturing. RCPC codes are well suited for constellations for which Euclidean and Hamming distances are equivalent (BPSK and 4-PSK). This paper introduces rate-compatible punctured trellis codes (RCPT) where rate compatibility and UEP are provided via progressive puncturing of symbols in a trellis. RCPT codes constitute a special class of codes designed to maximize residual Euclidean distances (RED) after symbol puncturing. They can be designed for any constellation, allowing for higher throughput than when restricted to using 4-PSK. We apply RCPC and RCPT to two embedded source coders: a perceptual subband coder and the ITU embedded ADPCM G.727 standard. Different operating modes with distinct source/channel bit allocation and UEP are defined. Each mode is optimal for a certain range of AWGN channel SNRs. Performance results using an 8-PSK constellation clearly illustrate the wide range of channel conditions at which the adaptive scheme using RCPT can operate. For an 8-PSK constellation, RCPT codes are compared to RCPC with bit interleaved coded modulation codes (RCPC-BICM). We also compare performance to RCPC codes used with a 4-PSK constellation.

This paper presents source and channel coding techniques for remote automatic speech recognition (ASR) systems. As a case study, line spectral pairs (LSP) extracted from the 6th order all-pole perceptual linear prediction (PLP) spectrum are transmitted and speech recognition features are then obtained. The LSPs, quantized using first-order predictive vector quantization (VQ) at 300 bps, provide recognition accuracy comparable to that of the baseline system with no quantization. A new soft decision channel decoding scheme appropriate for remote recognition is presented. The scheme outperforms commonly-used hard decision decoding in terms of error correction and error detection. The source and channel coding system operates at 500 bps and provides good digit recognition performance over a wide range of channel conditions.

This paper presents a multi-rate wideband speech coder with bit rates from 8 to 32 kb/s. The coder uses a splitband approach, where the input signal, sampled at 16 kHz, is split into two equal frequency bands from 0-4 kHz and 4-8 kHz, each of which is decimated to an 8 kHz sampling rate. The lower band is coded using the adaptive multi-rate (AMR) family of high-quality narrowband speech coders, while the higher band is represented by a simple but effective parametric model. A complete solution including this wideband speech coder, channel coding for various GSM channels, and dynamic rate adaptation, easily passed all Selection Rules and ranked second overall in the 3GPP AMR Wideband Selection Testing. Besides the high performance, additional advantages of the embedded split-band approach include ease of implementation, reduced complexity, and simplified interoperation with narrowband speech coders.

We introduce the concept of joint channel decoding and Viterbi recognition, by which the Viterbi recognizer is modified to take into account the confidence in the decoded feature after channel transmission. We present a metric for evaluating such confidence based on soft decision decoding. As a case study, we quantize MFCCs using predictive VQ. The overall source-channel coding scheme operating at a combined rate of 1 kbps is shown to provide good recognition accuracy over a wide range of Rayleigh fading channels. 1.

This paper presents an embedded joint source-channel coding scheme of speech. The source coder is an embedded variable bit rate perceptually based sub-band coder producing bits with different error sensitivities. The channel encoder is a rate compatible punctured trellis code (RCPT) which permits rate variability and unequal error protection by puncturing symbols. Furthermore, RCPT code design naturally incorporates large constellations, allowing high information rate per symbol. The embedded speech coder and the rate compatible puncturing of symbols provide the embeddibility of the joint coding scheme. The coder is robust to acoustic noise and produces good quality speech for a wide range of channel conditions (AWGN or fading), allowing digital transmission of speech with analog-like graceful degradation.