NIST 2007 Language Recognition Evaluation: From the Perspective of IIR

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Augmenting Translation Models with Simulated Acoustic Confusions for improved Spoken Language Translation

We propose a novel technique for adapting text-based statistical machine translation to deal with input from automatic speech recognition in spoken language translation tasks. We simulate likely misrecognition errors using only a source language pronunciation dictionary and language model (i.e., without an acoustic model), and use these to augment the phrase table of a standard MT system. The augmented system can thus recover from recognition errors during decoding using synthesized phrases. Using the outputs of five different English ASR systems as input, we find consistent and significant improvements in translation quality. Our proposed technique can also be used in conjunction with lattices as ASR output, leading to further improvements

Segregation of Vowels and Consonants in Human Auditory Cortex: Evidence for Distributed Hierarchical Organization

The speech signal consists of a continuous stream of consonants and vowels, which must be de- and encoded in human auditory cortex to ensure the robust recognition and categorization of speech sounds. We used small-voxel functional magnetic resonance imaging to study information encoded in local brain activation patterns elicited by consonant-vowel syllables, and by a control set of noise bursts. First, activation of anterior–lateral superior temporal cortex was seen when controlling for unspecific acoustic processing (syllables versus band-passed noises, in a “classic” subtraction-based design). Second, a classifier algorithm, which was trained and tested iteratively on data from all subjects to discriminate local brain activation patterns, yielded separations of cortical patches discriminative of vowel category versus patches discriminative of stop-consonant category across the entire superior temporal cortex, yet with regional differences in average classification accuracy. Overlap (voxels correctly classifying both speech sound categories) was surprisingly sparse. Third, lending further plausibility to the results, classification of speech–noise differences was generally superior to speech–speech classifications, with the no\ exception of a left anterior region, where speech–speech classification accuracies were significantly better. These data demonstrate that acoustic–phonetic features are encoded in complex yet sparsely overlapping local patterns of neural activity distributed hierarchically across different regions of the auditory cortex. The redundancy apparent in these multiple patterns may partly explain the robustness of phonemic representations

Advances in unlimited-vocabulary speech recognition for morphologically rich languages

Automatic speech recognition systems are devices or computer programs that convert human speech into text or make actions based on what is said to the system. Typical applications include dictation, automatic transcription of large audio or video databases, speech-controlled user interfaces, and automated telephone services, for example. If the recognition system is not limited to a certain topic and vocabulary, covering the words in the target languages as well as possible while maintaining a high recognition accuracy becomes an issue. The conventional way to model the target language, especially in English recognition systems, is to limit the recognition to the most common words of the language. A vocabulary of 60 000 words is usually enough to cover the language adequately for arbitrary topics. On the other hand, in morphologically rich languages, such as Finnish, Estonian and Turkish, long words can be formed by inflecting and compounding, which makes it difficult to cover the language adequately by vocabulary-based approaches. This thesis deals with methods that can be used to build efficient speech recognition systems for morphologically rich languages. Before training the statistical n-gram language models on a large text corpus, the words in the corpus are automatically segmented into smaller fragments, referred to as morphs. The morphs are then used as modelling units of the n-gram models instead of whole words. This makes it possible to train the model on the whole text corpus without limiting the vocabulary and enables the model to create even unseen words by joining morphs together. Since the segmentation algorithm is unsupervised and data-driven, it can be readily used for many languages. Speech recognition experiments are made on various Finnish recognition tasks and some of the experiments are also repeated on an Estonian task. It is shown that the morph-based language models reduce recognition errors when compared to word-based models. It seems to be important, however, that the n-gram models are allowed to use long morph contexts, especially if the morphs used by the model are short. This can be achieved by using growing and pruning algorithms to train variable-length n-gram models. The thesis also presents data structures that can be used for representing the variable-length n-gram models efficiently in recognition systems. By analysing the recognition errors made by Finnish recognition systems it is found out that speaker adaptive training and discriminative training methods help to reduce errors in different situations. The errors are also analysed according to word frequencies and manually defined error classes

Broad phonetic class definition driven by phone confusions

Intermediate representations between the speech signal and phones may be used to improve discrimination among phones that are often confused. These representations are usually found according to broad phonetic classes, which are defined by a phonetician. This article proposes an alternative data-driven method to generate these classes. Phone confusion information from the analysis of the output of a phone recognition system is used to find clusters at high risk of mutual confusion. A metric is defined to compute the distance between phones. The results, using TIMIT data, show that the proposed confusion-driven phone clustering method is an attractive alternative to the approaches based on human knowledge. A hierarchical classification structure to improve phone recognition is also proposed using a discriminative weight training method. Experiments show improvements in phone recognition on the TIMIT database compared to a baseline system

Speaker independent isolated word recognition

The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhibit non-linear temporal fluctuations and thus a high degree of redundancy. The initial part of this thesis considers various dynamic time warping techniques used for normalizing the temporal differences between speech patterns. Redundancy removal methods are also considered, and their effect on the recognition accuracy is assessed. Although the use of dynamic time warping algorithms provide considerable improvement in the accuracy of isolated word recognition schemes, the performance is ultimately limited by their poor ability to discriminate between acoustically similar words. Methods for enhancing the identification rate among acoustically similar words, by using common pattern features for similar sounding regions, are investigated. Pattern matching based, speaker independent systems, can only operate with a high recognition rate, by using multiple reference patterns for each of the words included in the vocabulary. These patterns are obtained from the utterances of a group of speakers. The use of multiple reference patterns, not only leads to a large increase in the memory requirements of the recognizer, but also an increase in the computational load. A recognition system is proposed in this thesis, which overcomes these difficulties by (i) employing vector quantization techniques to reduce the storage of reference patterns, and (ii) eliminating the need for dynamic time warping which reduces the computational complexity of the system. Finally, a method of identifying the acoustic structure of an utterance in terms of voiced, unvoiced, and silence segments by using fuzzy set theory is proposed. The acoustic structure is then employed to enhance the recognition accuracy of a conventional isolated word recognizer

Regularization of context-dependent deep neural networks with context-independent multi-task training

The use of context-dependent targets has become standard in hybrid DNN systems for automatic speech recognition. However, we argue that despite the use of state-tying, optimising to context-dependent targets can lead to over-fitting, and that discriminating between ar-bitrary tied context-dependent targets may not be optimal. We pro-pose a multitask learning method where the network jointly predicts context-dependent and monophone targets. We evaluate the method on a large-vocabulary lecture recognition task and show that it yields relative improvements of 3-10 % over baseline systems. Index Terms — deep neural networks, multitask learning, regu-larization 1

Effect of maturation on suprasegmental speech processing in full- and preterm infants: A mismatch negativity study

Infants born prematurely are at higher risk for later linguistic deficits present in delayed or atypical processing of phonetic and prosodic information. In order to be able to specify the nature of this atypical development, it is important to investigatethe role of early experience in language perception. According to the concept of Gonzalez -Gomez and Nazzi (2012)there is a special intrauterine sensitivity to the prosodic features of languages that should have a special role in language acquisition. Therefore, we may also assume that pre- and full-term infants having months difference in intrauterine experience show different maturation patterns of processing prosodic and phonetic information present at word level. The aim of our study was to investigate the effect of these differences on word stress patternvs. phoneme information processing. Two age groups of infants (6 and 10 month-olds) were included in our study. 21 of 46 of the total of infants investigated were prematurely born with low birth weight. W e used the mismatch negativity (MMN) event related brain potential (ERP)component, a widely used electrophysiological correlate of acoustic change detection, for testing the assumed developmental changes of phoneme and word stress discrimination. In a passive oddball paradigm we used a word as standard, a pseudo-word as phoneme deviant, and an illegally uttered word as stress deviant.Our results showed no differences in MMN responses in the phoneme deviant condition between the groups,meaning a relatively intact maturation of phoneme processing of preterm infants a s compared to their contemporaries. However, the mismatch responses measured in the stress condition revealed significant between - group differences. These results strengthen the view that the total length of intrauterine experience influences the time of emergence of prosodic processing