Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus

This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world. The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively

Continuous wavelet transform for analysis of speech prosody

Wavelet based time frequency representations of various signals are shown to reliably represent perceptually relevant patterns at various spatial and temporal scales in a noise robust way. Here we present a wavelet based visualization and analysis tool for prosodic patterns, in particular intonation. The suitability of the method is assessed by comparing its predictions for word prominences against manual labels in a corpus of 900 sentences. In addition, the method’s potential for visualization is demonstrated by a few example sentences which are compared to more traditional visualization methods. Finally, some further applications are suggested and the limitations of the method are discussed.Peer reviewe

A method for the extraction of phonetically-rich triphone sentences

A method is proposed for compiling a corpus of phonetically-rich triphone sentences; i.e., sentences with a high variety of triphones, distributed in a uniform fashion. Such a corpus is of interest for a wide range of contexts, from automatic speech recognition to speech therapy. We evaluated this method by building phonetically-rich corpora for Brazilian Portuguese. The data employed comes from Wikipedia’s dumps, which were converted into plain text, segmented and phonetically transcribed. The method consists of comparing the distance between the triphone distribution of the available sentences to na ideal uniform distribution, with equiprobable triphones. A greedy algorithm was implemented to recognize and evaluate the distance among sentences. A heuristic metric is proposed for pre-selecting sentences for the algorithm, in order to quicken its execution. The results show that, by applying the proposed metric, one can build corpora with more uniform triphone distributions

Concatenative speech synthesis: a Framework for Reducing Perceived Distortion when using the TD-PSOLA Algorithm

This thesis presents the design and evaluation of an approach to concatenative speech synthesis using the Titne-Domain Pitch-Synchronous OverLap-Add (I'D-PSOLA) signal processing algorithm. Concatenative synthesis systems make use of pre-recorded speech segments stored in a speech corpus. At synthesis time, the `best' segments available to synthesise the new utterances are chosen from the corpus using a process known as unit selection. During the synthesis process, the pitch and duration of these segments may be modified to generate the desired prosody. The TD-PSOLA algorithm provides an efficient and essentially successful solution to perform these modifications, although some perceptible distortion, in the form of `buzzyness', may be introduced into the speech signal. Despite the popularity of the TD-PSOLA algorithm, little formal research has been undertaken to address this recognised problem of distortion. The approach in the thesis has been developed towards reducing the perceived distortion that is introduced when TD-PSOLA is applied to speech. To investigate the occurrence of this distortion, a psychoacoustic evaluation of the effect of pitch modification using the TD-PSOLA algorithm is presented. Subjective experiments in the form of a set of listening tests were undertaken using word-level stimuli that had been manipulated using TD-PSOLA. The data collected from these experiments were analysed for patterns of co- occurrence or correlations to investigate where this distortion may occur. From this, parameters were identified which may have contributed to increased distortion. These parameters were concerned with the relationship between the spectral content of individual phonemes, the extent of pitch manipulation, and aspects of the original recordings. Based on these results, a framework was designed for use in conjunction with TD-PSOLA to minimise the possible causes of distortion. The framework consisted of a novel speech corpus design, a signal processing distortion measure, and a selection process for especially problematic phonemes. Rather than phonetically balanced, the corpus is balanced to the needs of the signal processing algorithm, containing more of the adversely affected phonemes. The aim is to reduce the potential extent of pitch modification of such segments, and hence produce synthetic speech with less perceptible distortion. The signal processingdistortion measure was developed to allow the prediction of perceptible distortion in pitch-modified speech. Different weightings were estimated for individual phonemes,trained using the experimental data collected during the listening tests.The potential benefit of such a measure for existing unit selection processes in a corpus-based system using TD-PSOLA is illustrated. Finally, the special-case selection process was developed for highly problematic voiced fricative phonemes to minimise the occurrence of perceived distortion in these segments. The success of the framework, in terms of generating synthetic speech with reduced distortion, was evaluated. A listening test showed that the TD-PSOLA balanced speech corpus may be capable of generating pitch-modified synthetic sentences with significantly less distortion than those generated using a typical phonetically balanced corpus. The voiced fricative selection process was also shown to produce pitch-modified versions of these phonemes with less perceived distortion than a standard selection process. The listening test then indicated that the signal processing distortion measure was able to predict the resulting amount of distortion at the sentence-level after the application of TD-PSOLA, suggesting that it may be beneficial to include such a measure in existing unit selection processes. The framework was found to be capable of producing speech with reduced perceptible distortion in certain situations, although the effects seen at the sentence-level were less than those seen in the previous investigative experiments that made use of word-level stimuli. This suggeststhat the effect of the TD-PSOLA algorithm cannot always be easily anticipated due to the highly dynamic nature of speech, and that the reduction of perceptible distortion in TD-PSOLA-modified speech remains a challenge to the speech community

Arabic Continuous Speech Recognition System using Sphinx-4

Speech is the most natural form of human communication and speech processing has been one of the most exciting areas of the signal processing. Speech recognition technology has made it possible for computer to follow human voice commands and understand human languages. The main goal of speech recognition area is to develop techniques and systems for speech input to machine and treat this speech to be used in many applications. As Arabic is one of the most widely spoken languages in the world. Statistics show that it is the first language (mother-tongue) of 206 million native speakers ranked as fourth after Mandarin, Spanish and English. In spite of its importance, research effort on Arabic Automatic Speech Recognition (ASR) is unfortunately still inadequate[7]. This thesis proposes and describes an efficient and effective framework for designing and developing a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The developing Arabic speech recognition system is based on the Carnegie Mellon university Sphinx tools. To build the system, we develop three basic components. The dictionary which contains all possible phonetic pronunciations of any word in the domain vocabulary. The second one is the language model such a model tries to capture the properties of a sequence of words by means of a probability distribution, and to predict the next word in a speech sequence. The last one is the acoustic model which will be created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word. The system use the rich and balanced database that contains 367 sentences, a total of 14232 words. The phonetic dictionary contains about 23,841 definitions corresponding to the database words. And the language model contains14233 mono-gram and 32813 bi-grams and 37771 tri-grams. The engine uses 3-emmiting states Hidden Markov Models (HMMs) for tri-phone-based acoustic models

HuComTech Multimodal Corpus Annotation

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Production and perception of speaker-specific phonetic detail at word boundaries

Experiments show that learning about familiar voices affects speech processing in many tasks. However, most studies focus on isolated phonemes or words and do not explore which phonetic properties are learned about or retained in memory. This work investigated inter-speaker phonetic variation involving word boundaries, and its perceptual consequences. A production experiment found significant variation in the extent to which speakers used a number of acoustic properties to distinguish junctural minimal pairs e.g. 'So he diced them'—'So he'd iced them'. A perception experiment then tested intelligibility in noise of the junctural minimal pairs before and after familiarisation with a particular voice. Subjects who heard the same voice during testing as during the familiarisation period showed significantly more improvement in identification of words and syllable constituents around word boundaries than those who heard different voices. These data support the view that perceptual learning about the particular pronunciations associated with individual speakers helps listeners to identify syllabic structure and the location of word boundaries