Digital processing of speech signals

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Detection, Estimation, and Modulation Theory

Efficient voice activity detection algorithms using long-term speech information

A personal communications system is described which includes components of software and hardware operating in conjunction with a personal computer. The user interface control software operates on a personal computer, preferably within the Microsoft Windows® environment. The software control system communicates with hardware components linked to the software through the personal computer serial communications port. The hardware components include telephone communication equipment, digital signal processors, and hardware to enable voice, fax and data communication with a remote site connected through a standard telephone line. The functions of the hardware components are controlled by control software operating within the hardware component and from the software components operating within the personal computer. The major functions of the system are a telephone function, a voice mail function, a fax manager function, a multi-media mail function, a show and tell function, a terminal function and an address book function. The telephone function allows the present system to operate, from the users perspective, as a conventional telephone using either hands-free, headset or handset operation. The telephone function is more sophisticated than a standard telephone in that the present system converts the voice into a digital signal which can be processed with echo cancellation, compressed, stored as digital data for later retrieval and transmitted as digital voice data concurrent with the transfer of digital information data.

Computer-based multifunction personal communications system.

An important drawback affecting most of the speech processing systems is the environmental noise and its harmful effect on the system performance. Examples of such systems are the new wireless communications voice services or digital hearing aid devices. In speech recognition, there are still technical barriers inhibiting such systems from meeting the demands of modern applications. Numerous noise reduction techniques have been developed to palliate the effect of the noise on the system performance and often require an estimate of the noise statistics obtained by means of a precise voice activity detector (VAD). Speech/non-speech detection is an unsolved problem in speech processing and affects numerous applications including robust speech recognition (Karray and Marting, 2003; Ramirez et al. 2003), discontinuous transmission (ITU, 1996; ETSI, 1999), real-time speech transmission on the Internet (Sangwan et al., 2002) or combined noise reduction and echo cancellation schemes in the context of telephony (Basbug et al., 2004; Gustafsson et al., 2002). The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. During the last decade, numerous researchers have developed different strategies for detecting speech on a noisy signal (Sohn et al., 1999; Cho and Kondoz, 2001; Gazor and Zhang, 2003, Armani et al., 2003) and have evaluated the influence of the VAD effectiveness on the performance of speech processing systems (Bouquin-Jeannes and Faucon, 1995). Most of the approaches have focussed on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules (Woo et al., 2000; Li et al., 2002; Marzinzik and Kollmeier, 2002). The different VAD methods include those based on energy thresholds (Woo et al., 2000), pitch detection (Chengalvarayan, 1999), spectrum analysis (Marzinzik and Kollmeier, 2002), zero-crossing rate (ITU, 1996), periodicity measure (Tucker, 1992), higher order statistics in the LPC residual domain (Nemer et al., 2001) or combinations of different features (ITU, 1993; ETSI, 1999; Tanyer and Ozer, 2000). This chapter shows a comprehensive approximation to the main challenges in voice activity detection, the different solutions that have been reported in a complete review of the state of the art and the evaluation frameworks that are normally used. The application of VADs for speech coding, speech enhancement and robust speech recognition systems is shown and discussed. Three different VAD methods are described and compared to standardized and

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Voice Activity Detection. Fundamentals and Speech Recognition System Robustness

Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new generation wireless communication systems. In this context, robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. This paper presents a voice detection algorithm which is robust to noisy environments, thanks to a new methodology adopted for the matching process. More specifically, the VAD proposed is based on a pattern recognition approach in which the matching phase is performed by a set of six fuzzy rules, trained by means of a new hybrid learning tool. A series of objective tests performed on a large speech database, varying the signal-to-noise ratio (SNR), the types of background noise, and the input signal level, showed that, as compared with the VAD standardized by ITU-T in Recommendation G.729 annex B, the fuzzy VAD, on average, achieves an improvement in reduction both of the activity factor of about 25% and of the clipping introduced of about 43%. Informal listening tests also confirm an improvement in the perceived speech quality.

A robust voice activity detector for wireless communications using soft computing

The paper proposes a performance evaluation and comparison of G.729, AMR, and fuzzy voice activity detection (FVAD) algorithms. The comparison was made using objective, psychoacoustic, and subjective parameters. A highly varied speech database was also set up to evaluate the extent to which VADs depend on language, the signal-to-noise ratio (SNR), or the power level.

Performance evaluation and comparison of G.729/AMR/fuzzy voice activity detectors

The recognition of emotional states is a relatively new technique in the field of machine learning. The paper presents the study and the performance results of a system for emotion classification using the architecture of a distributed speech recognition system (DSR). The features used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202-211 V1.1.1 standard. On the basis of the time trend of these parameters, over 3800 statistical parameters were extracted to characterize semantic units of varying length (sentences and words). Using the WEKA (Waikato Environment for Knowledge Analysis) software the most significant parameters for the classification of emotional states were selected and the results of various classification techniques were analysed. The results, obtained using both the Berlin Database of Emotional Speech (EMO-DB) and the Speech Under Simulated and Actual Stress (SUSAS) corpus, showed that the best performance is achieved using a support vector machine (SVM) trained with the sequential minimal optimization (SMO) algorithm, after normalizing and discretizing the input statistical parameters.

Speech Emotion Classification Using Machine Learning Algorithms

Multistyle classification of speech under stress using feature subset selection based on genetic algorithms

The paper proposes a performance evaluation and comparison of recent ITU-T and ETSI voice activity detection algorithms. The comparison was made using both objective and psychoacoustic parameters, so as to have reliable judgements that were close to subjective ones. A highly varied speech database was also set up to evaluate the extent to which VAD depend on language, the signal to noise ratio, or the power level.

Salvatore Casale

Papers

A robust voice activity detector for wireless communications using soft computing

Performance evaluation and comparison of G.729/AMR/fuzzy voice activity detectors

Speech Emotion Classification Using Machine Learning Algorithms

Multistyle classification of speech under stress using feature subset selection based on genetic algorithms

Performance evaluation and comparison of ITU-T/ETSI voice activity detectors