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

Confusion modelling for lip-reading

Lip-reading is mostly used as a means of communication by people with hearing di�fficulties. Recent work has explored the automation of this process, with the aim of building a speech recognition system entirely driven by lip movements. However, this work has so far produced poor results because of factors such as high variability of speaker features, diffi�culties in mapping from visual features to speech sounds, and high co-articulation of visual features. The motivation for the work in this thesis is inspired by previous work in dysarthric speech recognition [Morales, 2009]. Dysathric speakers have poor control over their articulators, often leading to a reduced phonemic repertoire. The premise of this thesis is that recognition of the visual speech signal is a similar problem to recog- nition of dysarthric speech, in that some information about the speech signal has been lost in both cases, and this brings about a systematic pattern of errors in the decoded output. This work attempts to exploit the systematic nature of these errors by modelling them in the framework of a weighted finite-state transducer cascade. Results indicate that the technique can achieve slightly lower error rates than the conventional approach. In addition, it explores some interesting more general questions for automated lip-reading

Spoken dialog systems based on online generated stochastic finite-state transducers

This is the author’s version of a work that was accepted for publication in Speech Communication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Speech Communication 83 (2016) 81–93. DOI 10.1016/j.specom.2016.07.011.In this paper, we present an approach for the development of spoken dialog systems based on the statistical modelization of the dialog manager. This work focuses on three points: the modelization of the dialog manager using Stochastic Finite-State Transducers, an unsupervised way to generate training corpora, and a mechanism to address the problem of coverage that is based on the online generation of synthetic dialogs. Our proposal has been developed and applied to a sport facilities booking task at the university. We present experimentation evaluating the system behavior on a set of dialogs that was acquired using the Wizard of Oz technique as well as experimentation with real users. The experimentation shows that the method proposed to increase the coverage of the Dialog System was useful to find new valid paths in the model to achieve the user goals, providing good results with real users. © 2016 Elsevier B.V. All rights reserved.This work is partially supported by the project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics (MINECO TIN2014-54288-C4-3-R).Hurtado Oliver, LF.; Planells Lerma, J.; Segarra Soriano, E.; Sanchís Arnal, E. (2016). Spoken dialog systems based on online generated stochastic finite-state transducers. Speech Communication. 83:81-93. https://doi.org/10.1016/j.specom.2016.07.011S81938

Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN

Call Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective Automated Speech Recognition system can accurately transcribe these calls for easy search through call history for specific context and content allowing automatic call monitoring, improving QoS through keyword search and sentiment analysis. ASR for Call Center requires more robustness as telephonic environment are generally noisy. Moreover, there are many low-resourced languages that are on verge of extinction which can be preserved with help of Automatic Speech Recognition Technology. Urdu is the $10^{th}$ most widely spoken language in the world, with 231,295,440 worldwide still remains a resource constrained language in ASR. Regional call-center conversations operate in local language, with a mix of English numbers and technical terms generally causing a "code-switching" problem. Hence, this paper describes an implementation framework of a resource efficient Automatic Speech Recognition/ Speech to Text System in a noisy call-center environment using Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid HMM-DNN approach allowed us to utilize the advantages of Neural Network with less labelled data. Adding CNN with TDNN has shown to work better in noisy environment due to CNN's additional frequency dimension which captures extra information from noisy speech, thus improving accuracy. We collected data from various open sources and labelled some of the unlabelled data after analysing its general context and content from Urdu language as well as from commonly used words from other languages, primarily English and were able to achieve WER of 5.2% with noisy as well as clean environment in isolated words or numbers as well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin