Digital processing of speech signals

Principal characteristics of speech speech production models speech analysis and analysis-synthesis systems linear predictive coding (LPC) analysis speech coding speech synthesis speech recognition future directions of speech processing. Appendices: convolution and z-transform vector quantization algorithm neural nests.

Digital speech processing, synthesis, and recognition

The isolated word speech recognition system based on dynamic time warping (DTW) has been developed. Speaker adaptation is performed using speaker recognition techniques. Vector quantization is used to create reference templates for speaker recognition. Linear predictive coding (LPC) parameters are used as features for recognition. Performance is evaluated using 12 words of Lithuanian language pronounced ten times by ten speakers.

Development of Isolated Word Speech Recognition System

Data transmission over GSM voice channel using digital modulation technique based on autoregressive modeling of speech production

Although the development of state-of-the-art speaker recognition systems has shown considerable progress in the last decade, performance levels of these systems do not as yet seem to warrant large-scale introduction in anything other than relatively low-risk applications. Conditions typical of the forensic context such as differences in recording equipment and transmission channels, the presence of background noise and of variation due to differences in communicative context continue to pose a major challenge. Consequently, the impact of automatic speaker recognition technology on the forensic scene has been relatively modest and forensic speaker identification practice remains heavily dominated by the use of a wide variety of largely subjective procedures. While recent developments in the interpretation of the evidential value of forensic evidence clearly favour methods that make it possible for results to be expressed in terms of a likelihood ratio, unlike automatic procedures, traditional methods in the field of speaker identification do not generally meet this requirement. However, conclusions in the form of a binary yes/no-decision or a qualified statement of the probability of the hypothesis rather than the evidence are increasingly criticised for being logically flawed. Against this background, the need to put alternative validation procedures in place is becoming more widely accepted. Although speaker identification by earwitnesses differs in some important respects from the much more widely studied field of eyewitness identification, there are sufficient parallels between the two for speaker identification by earwitnesses to benefit greatly from a close study of the guidelines that have been proposed for the administration of line-ups in the visual domain. Some of the central notions are briefly discussed. Rapid technical developments in the world of telecommunications in which speech and data are increasingly transmitted through the same communication channels may soon blunt the efficacy of traditional telephone interception as an investigative and evidential tool. The gradual shift from analogue to digital recording media and the increasingly widespread availability of digital sound processing equipment as well as its ease of operation make certain types of manipulation of audio recordings comparatively easy to perform. If done competently, such manipulation may leave no traces and may therefore well be impossible to detect. Authorship attribution is another forensic area that has had a relatively chequered history. The rapid increase in the use of electronic writing media including e-mail, sms, and the use of ink jet printers at the expense of typewritten and to a lesser extent hand-written texts reduces the opportunities of authorship attribution by means of traditional document examination techniques and may create a greater demand for linguistic expertise in this area. A survey is provided of ongoing work in the area, based on reactions to a questionnaire sent out earlier this year.

Forensic speech and audio analysis forensic linguistics

The paper deals with the use of formant features in dynamic time warping based speech recognition. These features can be simply visualized and give a new insight into understanding the reasons of speech recognition errors. The formant feature extraction method, based on the singular prediction polynomials, has been applied in recognition of isolated words. However, the speech recognition performance depends on the order of singular prediction polynomials, whether symmetric or antisymmetric singular prediction polynomials are used for recognition and as well on the fact even or odd order of these polynomials is chosen. Also, it is important to know how informative separate formants are, how the speech recognition results depend on other parameters of the recognition system such as: analysis frame length, number of the formants used in recognition, frequency scale used for representation of formant features, and the preemphasis filter parameters. Properly choosing the processing parameters, it is possible to optimize the speech recognition performance.

The aim of our current investigation is to optimize formant feature based isolated word recognition performance by varying processing parameters of the recognition system as well as to find improvements of the recognition system which could make it more robust to white noise. The optimization experiments were carried out using speech records of 111 Lithuanian words. The speech signals were recorded in the conventional room environment (SNR = 30 dB). Then the white noise was generated at a predefined level (65 dB, 60 dB and 55 dB) and added to the test utterances. The recognition performance was evaluated at various noise levels.

The optimization experiments allowed us to improve considerably the performance of the formant feature based speech recognition system and made the system more robust to white noise.

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Optimization of Formant Feature Based Speech Recognition

The scope of the paper is animation facilities of computer algebra systems (CAS). Animation offers opportunities for visualization of complex mathematical concepts, provides convincing demonstration of ideas and influence of quantities or parameters, helps to generate hypothesis, encourages exploration. Animation can be used to demonstrate many mathematical concepts that are difficult to explain verbally or to show with static pictures. Using animation allows students to explore, experiment and visualize mathematics as a dynamic process. But CAS creates only opportunities. The problem remains for users to realize this potential. So features of CAS such as ease of use, convenience of procedures are important for teaching and learning. The paper deals with animation features of the three most popular CAS - Maple, Matlab, Mathcad and their usefulness in education. The results of practical use of the three CAS in teaching animation procedures are discussed and students' opinion about animation tools of CAS is presented.

Animation tools of CAS for dynamic exploration of mathematics

A possibility to use the formant features (FF) in the user-dependent isolated word recognition has been investigated. The word recognition was performed using a dynamic time-warping technique. Several methods of the formant feature extraction were compared and a method based on the singular prediction polynomials has been proposed for the recognition of isolated words. Recognition performance of the proposed method was compared to that of the linear prediction coding (LPC) and LPC-derived cepstral features (LPCC). In total, 111 Lithuanian words were used in the recognition experiment. The recognition performance was evaluated at various noise levels. The experiments have shown that the formant features calculated from the singular prediction polynomials are more reliable than the LPC and LPCC features at all noise levels.

On the Use of the Formant Features in the Dynamic Time Warping Based Recognition of Isolated Words

The paper deals with the use of dynamic programming for word endpoint detection in isolated word recognition. Endpoint detection is based on likelihood maximization. Expectation maximization approach is used to deal with the problem of unknown parameters. Speech signal and background noise energy is used as features for making decision. Performance of the proposed approach was evaluated using isolated Lithuanian words speech corpus.

Antanas Lipeika

Papers

Development of Isolated Word Speech Recognition System

Optimization of Formant Feature Based Speech Recognition

Animation tools of CAS for dynamic exploration of mathematics

On the Use of the Formant Features in the Dynamic Time Warping Based Recognition of Isolated Words

Word Endpoint Detection Using Dynamic Programming