In this paper, we derive an uncertainty decoding rule for automatic speech recognition (ASR), which accounts for both corrupted observations and inter-frame correlation. The conditional independence assumption, prevalent in hidden Markov model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned on the complete observed feature vector sequence. This is a more informative posterior than one conditioned only on the current observation. The novel decoding is used to obtain a transmission-error robust remote ASR system, where the speech capturing unit is connected to the decoder via an error-prone communication network. We show how the clean speech posterior can be computed for communication links being characterized by either bit errors or packet loss. Recognition results are presented for both distributed and network speech recognition, where in the latter case common voice-over-IP codecs are employed.

In this paper we introduce soft features of variable resolution for robust distributed speech recognition over channels exhibiting packet losses. The underlying rationale is that lost feature vectors can never be reconstructed perfectly and therefore reconstruction is carried out at a lower resolution than the resolution of the originally sent features. By doing so, enormous reductions in computational effort can be achieved at a graceful or even no degradation in word accuracy. In experiments conducted on the Aurora II database we obtained for example a reduction of a factor of 30 in computation time for the reconstruction of the soft features without an effect on the word error rate. The proposed method is fully compatible with the ETSI DSR standard, as there are no changes involved in the front-end processing and the transmission format. Index Terms: distributed speech recognition, error concealment, uncertainty decoding, soft feature

Soft-feature based speech recognition, which is an example of uncertainty decoding, has been proven to be a robust error mitigation method for distributed speech recognition over wireless channels exhibiting bit errors. In this paper we extend this concept to packet-oriented transmissions. The a posteriori probability density function of the lost feature vector, given the closest received neighbours, is computed. In the experiments, the nearest frame repetition, which is shown to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts. Taking the variance into account at the speech recognition stage results in superior performance compared to classical schemes using point estimates. A computationally and memory efficient implementation of the proposed packet loss compensation scheme based on table lookup is presented

The accuracy of distributed speech recognition has been shown to be very sensitive to errors occurring during transmission. One reason for this is that the classifier, usually trained under error free conditions, is unable to cope with the mismatch between an error free and error prone channel. In this paper we present a novel decision rule for classification which is able to account for channel errors. To achieve this, the classical Bayesian speech recognition approach has been reformulated for the server side, where the observation is known only to the extent, as is given by its a posteriori density function. We present a method to estimate the a posteriori density which is based on a Markov model of the source, which captures correlations of both static and dynamic features. A practical implementation is given, accompanied by experimental results for distributed speech recognition over an IP-network. Index Terms: distributed speech recognition, channel robustness, predictive classification.

In this paper we present a comparison of the recently proposed soft-feature distributed speech recognition (SFDSR) with the two evaluated candidate codecs for speech enabled services over wireless networks: adaptive multirate codec (AMR) and the ETSI extended advanced front-end for distributed speech recognition (XAFE). It is shown that SFDSR achieves the best recognition performance on a simulated GSM transmission, followed by XAFE and AMR. We also present some new results concerning SFDSR which demonstrate the versatility of the approach. Further, a simple method is introduced which considerably reduces the computational effort.

The transmission errors in a wireless or packet oriented network may dramatically decrease the performance of a distributed speech recognition (DSR) system. Error concealment has been shown to be an effective way to mantain an acceptable word error rate when dealing with error prone communication channels. In this paper we propose an extension of our previously introduced soft features approach for the case that the soft-output of the channel decoder is not available at the server side of the DSR system. We found a simple method to estimate bit reliability information which still gives good speech recognition results. It is shown that some other error concealment schemes turn out to be special cases of the method proposed here. 1.

A major drawback of distributed versus terminal-based speech recognition is the fact that transmission errors can lead to degraded recognition performance. In this paper we employ soft features to mitigate the effect of bit errors on wireless transmission links: At the receiver a posteriori probabilities of the transmitted feature vectors are computed by combining bit reliability information provided by the channel decoder and a priori knowledge about residual redundancy in the feature vectors. While the first-order moment of the a posteriori probability function is the MMSE estimate, the second-order moment is a measure of the uncertainty in the reconstructed features. We conducted realistic simulations of GSM transmission and achieved significant improvements in word accuracy compared to the error mitigation strategy described in the ETSI standard.