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 recognition accuracy on CVSD speech under mismatched conditions

Emerging technology in mobile communications is seeing increasingly high acceptance as a preferred choice for last-mile communication. There have been a wide range of techniques to achieve signal compression to suit to the smaller bandwidths available on mobile communication channels; but speech recognition methods have seen success mostly only in controlled speech environments. However, designing of speech recognition systems for mobile communications is crucial in order to provide voice enabled command and control and for applications like Mobile Voice Commerce. Continuously Variable Slope Delta (CVSD) modulation, a technique for low bitrate coding of speech, has been in use particularly in military wireless environments for over 30 years, and is now also adopted by BlueTooth. CVSD is particularly suitable for Internet and mobile environments due to its robustness against transmission errors, and simplicity of implementation and the absence of a need for synchronization. In this paper, we study some characteristics of the CVSD speech in the context of robust recognition of compressed speech, and present two methods of improving the recognition accuracy in Automatic Speech Recognition (ASR) systems. We study the characteristics of the features extracted for ASR and how they relate to the corresponding features computed from Pulse Coded Modulation (PCM) speech and apply this relation to correct the CVSD features to improve recognition accuracy. Secondly we show that the ASR done on bit-streams directly, gives a good recognition accuracy and when combined with our approach gives a better accuracy

An Application of SVM to Lost Packets Reconstruction in Voice-Enabled Services

Voice over IP (VoIP) is becoming very popular due to the huge range of services that can be implemented by integrating different media (voice, audio, data, etc.). Besides, voice-enabled interfaces for those services are being very actively researched. Nevertheless the impoverishment of voice quality due to packet losses severely affects the speech recognizers supporting those interfaces ([8]). In this paper, we have compared the usual lost packets reconstruction method with an SVM-based one that outperforms previous results

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

A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits