Evaluation of speech unit modelling for HMM-based speech synthesis for Arabic

International audienceThis paper investigates the use of hidden Markov models (HMM) for Modern Standard Arabic speech synthesis. HMM-basedspeech synthesis systems require a description of each speech unit with a set of contextual features that specifies phonetic,phonological and linguistic aspects. To apply this method to Arabic language, a study of its particularities was conductedto extract suitable contextual features. Two phenomena are highlighted: vowel quantity and gemination. This work focuseson how to model geminated consonants (resp. long vowels), either considering them as fully-fledged phonemes or as thesame phonemes as their simple (resp. short) counterparts but with a different duration. Four modelling approaches have beenproposed for this purpose. Results of subjective and objective evaluations show that there is no important difference betweendifferentiating modelling units associated to geminated consonants (resp. long vowels) from modelling units associated tosimple consonants (resp. short vowels) and merging them as long as gemination and vowel quantity information is includedin the set of features

Quality of media traffic over Lossy internet protocol networks: Measurement and improvement.

Voice over Internet Protocol (VoIP) is an active area of research in the world of communication. The high revenue made by the telecommunication companies is a motivation to develop solutions that transmit voice over other media rather than the traditional, circuit switching network. However, while IP networks can carry data traffic very well due to their besteffort nature, they are not designed to carry real-time applications such as voice. As such several degradations can happen to the speech signal before it reaches its destination. Therefore, it is important for legal, commercial, and technical reasons to measure the quality of VoIP applications accurately and non-intrusively. Several methods were proposed to measure the speech quality: some of these methods are subjective, others are intrusive-based while others are non-intrusive. One of the non-intrusive methods for measuring the speech quality is the E-model standardised by the International Telecommunication Union-Telecommunication Standardisation Sector (ITU-T). Although the E-model is a non-intrusive method for measuring the speech quality, but it depends on the time-consuming, expensive and hard to conduct subjective tests to calibrate its parameters, consequently it is applicable to a limited number of conditions and speech coders. Also, it is less accurate than the intrusive methods such as Perceptual Evaluation of Speech Quality (PESQ) because it does not consider the contents of the received signal. In this thesis an approach to extend the E-model based on PESQ is proposed. Using this method the E-model can be extended to new network conditions and applied to new speech coders without the need for the subjective tests. The modified E-model calibrated using PESQ is compared with the E-model calibrated using i ii subjective tests to prove its effectiveness. During the above extension the relation between quality estimation using the E-model and PESQ is investigated and a correction formula is proposed to correct the deviation in speech quality estimation. Another extension to the E-model to improve its accuracy in comparison with the PESQ looks into the content of the degraded signal and classifies packet loss into either Voiced or Unvoiced based on the received surrounding packets. The accuracy of the proposed method is evaluated by comparing the estimation of the new method that takes packet class into consideration with the measurement provided by PESQ as a more accurate, intrusive method for measuring the speech quality. The above two extensions for quality estimation of the E-model are combined to offer a method for estimating the quality of VoIP applications accurately, nonintrusively without the need for the time-consuming, expensive, and hard to conduct subjective tests. Finally, the applicability of the E-model or the modified E-model in measuring the quality of services in Service Oriented Computing (SOC) is illustrated

Fractal based speech recognition and synthesis

Transmitting a linguistic message is most often the primary purpose of speech com­munication and the recognition of this message by machine that would be most useful. This research consists of two major parts. The first part presents a novel and promis­ing approach for estimating the degree of recognition of speech phonemes and makes use of a new set of features based fractals. The main methods of computing the frac­tal dimension of speech signals are reviewed and a new speaker-independent speech recognition system developed at De Montfort University is described in detail. Fi­nally, a Least Square Method as well as a novel Neural Network algorithm is employed to derive the recognition performance of the speech data. The second part of this work studies the synthesis of speech words, which is based mainly on the fractal dimension to create natural sounding speech. The work shows that by careful use of the fractal dimension together with the phase of the speech signal to ensure consistent intonation contours, natural-sounding speech synthesis is achievable with word level speech. In order to extend the flexibility of this framework, we focused on the filtering and the compression of the phase to maintain and produce natural sounding speech. A ‘naturalness level’ is achieved as a result of the fractal characteristic used in the synthesis process. Finally, a novel speech synthesis system based on fractals developed at De Montfort University is discussed. Throughout our research simulation experiments were performed on continuous speech data available from the Texas Instrument Massachusetts institute of technology ( TIMIT) database, which is designed to provide the speech research community with a standarised corpus for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition system

Speech quality prediction for voice over Internet protocol networks

Merged with duplicate record 10026.1/878 on 03.01.2017 by CS (TIS). Merged with duplicate record 10026.1/1657 on 15.03.2017 by CS (TIS)This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.IP networks are on a steep slope of innovation that will make them the long-term carrier of all types of traffic, including voice. However, such networks are not designed to support real-time voice communication because their variable characteristics (e.g. due to delay, delay variation and packet loss) lead to a deterioration in voice quality. A major challenge in such networks is how to measure or predict voice quality accurately and efficiently for QoS monitoring and/or control purposes to ensure that technical and commercial requirements are met. Voice quality can be measured using either subjective or objective methods. Subjective measurement (e.g. MOS) is the benchmark for objective methods, but it is slow, time consuming and expensive. Objective measurement can be intrusive or non-intrusive. Intrusive methods (e.g. ITU PESQ) are more accurate, but normally are unsuitable for monitoring live traffic because of the need for a reference data and to utilise the network. This makes non-intrusive methods(e.g. ITU E-model) more attractive for monitoring voice quality from IP network impairments. However, current non-intrusive methods rely on subjective tests to derive model parameters and as a result are limited and do not meet new and emerging applications. The main goal of the project is to develop novel and efficient models for non-intrusive speech quality prediction to overcome the disadvantages of current subjective-based methods and to demonstrate their usefulness in new and emerging VoIP applications. The main contributions of the thesis are fourfold: (1) a detailed understanding of the relationships between voice quality, IP network impairments (e.g. packet loss, jitter and delay) and relevant parameters associated with speech (e.g. codec type, gender and language) is provided. An understanding of the perceptual effects of these key parameters on voice quality is important as it provides a basis for the development of non-intrusive voice quality prediction models. A fundamental investigation of the impact of the parameters on perceived voice quality was carried out using the latest ITU algorithm for perceptual evaluation of speech quality, PESQ, and by exploiting the ITU E-model to obtain an objective measure of voice quality. (2) a new methodology to predict voice quality non-intrusively was developed. The method exploits the intrusive algorithm, PESQ, and a combined PESQ/E-model structure to provide a perceptually accurate prediction of both listening and conversational voice quality non-intrusively. This avoids time-consuming subjective tests and so removes one of the major obstacles in the development of models for voice quality prediction. The method is generic and as such has wide applicability in multimedia applications. Efficient regression-based models and robust artificial neural network-based learning models were developed for predicting voice quality non-intrusively for VoIP applications. (3) three applications of the new models were investigated: voice quality monitoring/prediction for real Internet VoIP traces, perceived quality driven playout buffer optimization and perceived quality driven QoS control. The neural network and regression models were both used to predict voice quality for real Internet VoIP traces based on international links. A new adaptive playout buffer and a perceptual optimization playout buffer algorithms are presented. A QoS control scheme that combines the strengths of rate-adaptive and priority marking control schemes to provide a superior QoS control in terms of measured perceived voice quality is also provided. (4) a new methodology for Internet-based subjective speech quality measurement which allows rapid assessment of voice quality for VoIP applications is proposed and assessed using both objective and traditional MOS test methods

DWT-DCT-Based Data Hiding for Speech Bandwidth Extension

The limited narrowband frequency range, about 300-3400Hz, used in telephone network channels results in less intelligible and poor-quality telephony speech. To address this drawback, a novel robust speech bandwidth extension using Discrete Wavelet Transform- Discrete Cosine Transform Based Data Hiding (DWTDCTBDH) is proposed. In this technique, the missing speech information is embedded in the narrowband speech signal. The embedded missing speech information is recovered steadily at the receiver end to generate a wideband speech of considerably better quality. The robustness of the proposed method to quantization and channel noises is confirmed by the mean square error test. The enhancement in the quality of reconstructed wideband speech of the proposed method over conventional methods is reasserted by subjective listening and objective tests

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

Using automatic speech recognition to evaluate Arabic to English transliteration

Increased travel and international communication has led to an increased need for transliteration of Arabic proper names for people, places, technical terms and organisations. There are a variety of available Arabic to English transliteration systems such as Unicode, the Buckwalter Arabic transliteration, and ArabTeX. The transliteration tables have been developed and used by researchers for many years, but there are only limited attempts to evaluate and compare different transliteration systems. This thesis investigates whether or not speech recognition technology could be used to evaluate different Arabic-English transliteration systems. In order to do so there were 5 main objectives: firstly, to investigate the possibility of using English speech recognition engines to recognize Arabic words; secondly, to establish the possibility of automatic transliteration of diacritised Arabic words for the purpose of creating a vocabulary for the speech recognition engine; thirdly, to explore the possibility of automatically generating transliterations of non diacritised Arabic words; fourthly to construct a general method to compare and evaluate different transliteration; and finally, to test the system and use it to experiment with new transliterations ideas

Speaker normalisation for large vocabulary multiparty conversational speech recognition

One of the main problems faced by automatic speech recognition is the variability of the testing conditions. This is due both to the acoustic conditions (different transmission channels, recording devices, noises etc.) and to the variability of speech across different speakers (i.e. due to different accents, coarticulation of phonemes and different vocal tract characteristics). Vocal tract length normalisation (VTLN) aims at normalising the acoustic signal, making it independent from the vocal tract length. This is done by a speaker specific warping of the frequency axis parameterised through a warping factor. In this thesis the application of VTLN to multiparty conversational speech was investigated focusing on the meeting domain. This is a challenging task showing a great variability of the speech acoustics both across different speakers and across time for a given speaker. VTL, the distance between the lips and the glottis, varies over time. We observed that the warping factors estimated using Maximum Likelihood seem to be context dependent: appearing to be influenced by the current conversational partner and being correlated with the behaviour of formant positions and the pitch. This is because VTL also influences the frequency of vibration of the vocal cords and thus the pitch. In this thesis we also investigated pitch-adaptive acoustic features with the goal of further improving the speaker normalisation provided by VTLN. We explored the use of acoustic features obtained using a pitch-adaptive analysis in combination with conventional features such as Mel frequency cepstral coefficients. These spectral representations were combined both at the acoustic feature level using heteroscedastic linear discriminant analysis (HLDA), and at the system level using ROVER. We evaluated this approach on a challenging large vocabulary speech recognition task: multiparty meeting transcription. We found that VTLN benefits the most from pitch-adaptive features. Our experiments also suggested that combining conventional and pitch-adaptive acoustic features using HLDA results in a consistent, significant decrease in the word error rate across all the tasks. Combining at the system level using ROVER resulted in a further significant improvement. Further experiments compared the use of pitch adaptive spectral representation with the adoption of a smoothed spectrogram for the extraction of cepstral coefficients. It was found that pitch adaptive spectral analysis, providing a representation which is less affected by pitch artefacts (especially for high pitched speakers), delivers features with an improved speaker independence. Furthermore this has also shown to be advantageous when HLDA is applied. The combination of a pitch adaptive spectral representation and VTLN based speaker normalisation in the context of LVCSR for multiparty conversational speech led to more speaker independent acoustic models improving the overall recognition performances

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences