Using a low-bit rate speech enhancement variable post-filter as a speech recognition system pre-filter to improve robustness to GSM speech

Includes bibliographical references.Performance of speech recognition systems degrades when they are used to recognize speech that has been transmitted through GS1 (Global System for Mobile Communications) voice communication channels (GSM speech). This degradation is mainly due to GSM speech coding and GSM channel noise on speech signals transmitted through the network. This poor recognition of GSM channel speech limits the use of speech recognition applications over GSM networks. If speech recognition technology is to be used unlimitedly over GSM networks recognition accuracy of GSM channel speech has to be improved. Different channel normalization techniques have been developed in an attempt to improve recognition accuracy of voice channel modified speech in general (not specifically for GSM channel speech). These techniques can be classified into three broad categories, namely, model modification, signal pre-processing and feature processing techniques. In this work, as a contribution toward improving the robustness of speech recognition systems to GSM speech, the use of a low-bit speech enhancement post-filter as a speech recognition system pre-filter is proposed. This filter is to be used in recognition systems in combination with channel normalization techniques

Analytic Assessment of Telephone Transmission Impact on ASR Performance Using a Simulation Model

This paper addresses the impact of telephone transmission channels on automatic speech recognition (ASR) performance. A real-time simulation model is described and implemented, which allows impairments that are encountered in traditional as well as modern (mobile, IP-based) networks to be flexibly and efficiently generated. The model is based on input parameters which are known to telephone network planners; thus, it can be applied without measuring specific network characteristics. It can be used for an analytic assessment of the impact of channel impairments on ASR performance, for producing training material with defined transmission characteristics, or for testing spoken dialogue systems in realistic network environments. In the present paper, we present an investigation of the first point. Two speech recognizers which are integrated into a spoken dialogue system for information retrieval are assessed in relation to controlled amounts of transmission degradations. The measured ASR performance degradation is compared to speech quality degradation in human-human communication. It turns out that different behavior can be expected for some impairments. This fact has to be taken into account in both telephone network planning as well as in speech and language technology development

Recognizing GSM Digital Speech

The Global System for Mobile (GSM) environment encompasses three main problems for automatic speech recognition (ASR) systems: noisy scenarios, source coding distortion, and transmission errors. The first one has already received much attention; however, source coding distortion and transmission errors must be explicitly addressed. In this paper, we propose an alternative front-end for speech recognition over GSM networks. This front-end is specially conceived to be effective against source coding distortion and transmission errors. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bitstream) instead of decoding it and subsequently extracting the feature vectors. This approach offers two significant advantages. First, the recognition system is only affected by the quantization distortion of the spectral envelope. Thus, we are avoiding the influence of other sources of distortion as a result of the encoding-decoding process. Second, when transmission errors occur, our front-end becomes more effective since it is not affected by errors in bits allocated to the excitation signal. We have considered the half and the full-rate standard codecs and compared the proposed front-end with the conventional approach in two ASR tasks, namely, speaker-independent isolated digit recognition and speaker-independent continuous speech recognition. In general, our approach outperforms the conventional procedure, for a variety of simulated channel conditions. Furthermore, the disparity increases as the network conditions worsen

IMPROVING THE AUTOMATIC RECOGNITION OF DISTORTED SPEECH

Automatic speech recognition has a wide variety of uses in this technological age, yet speech distortions present many difficulties for accurate recognition. The research presented provides solutions that counter the detrimental effects that some distortions have on the accuracy of automatic speech recognition. Two types of speech distortions are focused on independently. They are distortions due to speech coding and distortions due to additive noise. Compensations for both types of distortion resulted in decreased recognition error.Distortions due to the speech coding process are countered through recognition of the speech directly from the bitstream, thus eliminating the need for reconstruction of the speech signal and eliminating the distortion caused by it. There is a relative difference of 6.7% between the recognition error rate of uncoded speech and that of speech reconstructed from MELP encoded parameters. The relative difference between the recognition error rate for uncoded speech and that of encoded speech recognized directly from the MELP bitstream is 3.5%. This 3.2 percentage point difference is equivalent to the accurate recognition of an additional 334 words from the 12,863 words spoken.Distortions due to noise are offset through appropriate modification of an existing noise reduction technique called minimum mean-square error log spectral amplitude enhancement. A relative difference of 28% exists between the recognition error rate of clean speech and that of speech with additive noise. Applying a speech enhancement front-end reduced this difference to 22.2%. This 5.8 percentage point difference is equivalent to the accurate recognition of an additional 540 words from the 12,863 words spoken

Contribuciones al reconocimiento robusto de habla en redes de comunicaciones mediante transparametrización

La creciente influencia de las redes de comunicaciones en todos los ámbitos de la vida moderna hace que cada vez sean más los servicios que se ofrecen a través de ellas, y dado que la comunicación oral es la forma más natural de comunicación humana, las tecnologías del habla juegan un rol importante en nuestra sociedad. Por este motivo, en esta tesis planteamos una serie de contribuciones al reconocimiento de habla en entornos de redes de comunicaciones, utilizando la técnica reconocimiento mediante transparametrización (RMT) sobre los dos tipos de redes que más cobertura tienen hoy en día: Internet y la telefonía celular. En particular, mejoramos la robustez ya demostrada de la técnica RMT frente a la distorsión por codificación y los errores de transmisión, y extendemos el análisis a casos con ruido de ambiente. En primer lugar, proponemos un procedimiento mejorado de estimación de la energía. En segundo lugar, aplicamos una técnica complementaria al RMT consistente en un filtrado del espectro de modulación, demostrando su eficacia en el entorno Internet. Además, y específicamente para el entorno UMTS proponemos una extensión de parámetros fundamentada en la protección que realiza el codificador de canal normativo y que consigue hacer un uso eficaz de los parámetros más protegidos por el codificador de canal, en beneficio de la robustez del sistema de reconocimiento. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Nowadays, the modern communication networks play an outstanding role in our everyday life and the number of services offered through them is continuously increasing. As the interfaces to these services become more natural, they tend to embed speech technologies so that the human-to-machine communication mimics (to some extent) the human-to-human communication. In this context, this thesis tackles the problem of automatic speech recognition (ASR) in communication-centered environments. In particular, our contributions focus on the bitstream-based approach to ASR, which has already proved to be robust, in two of the most relevant communication scenarios: Internet and universal mobile telecommunication system (UMTS) networks. In this thesis we propose some techniques to improve the robustness of the ASR systems against the distortions resulting from the source coding and the transmission errors. For the voice over IP scenario, we propose an improved method for energy estimation and an additional technique based on filtering the modulation spectrum so that we are able to jointly deal with communication-related distortions and background noise. For the UMTS scenario, besides an improved energy estimation method, in this thesis we propose an extended feature vector that relies on the unequal error protection mechanism implemented in the channel codec. This extended feature vector makes an effective use of the most protected parameters in the bitstream to provide the ASR system with an enhanced robustness

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes