Spoken command recognition for robotics

In this thesis, I investigate spoken command recognition technology for robotics. While high robustness is expected, the distant and noisy conditions in which the system has to operate make the task very challenging. Unlike commercial systems which all rely on a "wake-up" word to initiate the interaction, the pipeline proposed here directly detect and recognizes commands from the continuous audio stream. In order to keep the task manageable despite low-resource conditions, I propose to focus on a limited set of commands, thus trading off flexibility of the system against robustness. Domain and speaker adaptation strategies based on a multi-task regularization paradigm are first explored. More precisely, two different methods are proposed which rely on a tied loss function which penalizes the distance between the output of several networks. The first method considers each speaker or domain as a task. A canonical task-independent network is jointly trained with task-dependent models, allowing both types of networks to improve by learning from one another. While an improvement of 3.2% on the frame error rate (FER) of the task-independent network is obtained, this only partially carried over to the phone error rate (PER), with 1.5% of improvement. Similarly, a second method explored the parallel training of the canonical network with a privileged model having access to i-vectors. This method proved less effective with only 1.2% of improvement on the FER. In order to make the developed technology more accessible, I also investigated the use of a sequence-to-sequence (S2S) architecture for command classification. The use of an attention-based encoder-decoder model reduced the classification error by 40% relative to a strong convolutional neural network (CNN)-hidden Markov model (HMM) baseline, showing the relevance of S2S architectures in such context. In order to improve the flexibility of the trained system, I also explored strategies for few-shot learning, which allow to extend the set of commands with minimum requirements in terms of data. Retraining a model on the combination of original and new commands, I managed to achieve 40.5% of accuracy on the new commands with only 10 examples for each of them. This scores goes up to 81.5% of accuracy with a larger set of 100 examples per new command. An alternative strategy, based on model adaptation achieved even better scores, with 68.8% and 88.4% of accuracy with 10 and 100 examples respectively, while being faster to train. This high performance is obtained at the expense of the original categories though, on which the accuracy deteriorated. Those results are very promising as the methods allow to easily extend an existing S2S model with minimal resources. Finally, a full spoken command recognition system (named iCubrec) has been developed for the iCub platform. The pipeline relies on a voice activity detection (VAD) system to propose a fully hand-free experience. By segmenting only regions that are likely to contain commands, the VAD module also allows to reduce greatly the computational cost of the pipeline. Command candidates are then passed to the deep neural network (DNN)-HMM command recognition system for transcription. The VoCub dataset has been specifically gathered to train a DNN-based acoustic model for our task. Through multi-condition training with the CHiME4 dataset, an accuracy of 94.5% is reached on VoCub test set. A filler model, complemented by a rejection mechanism based on a confidence score, is finally added to the system to reject non-command speech in a live demonstration of the system

Conditioning Text-to-Speech synthesis on dialect accent: a case study

Modern text-to-speech systems are modular in many different ways. In recent years, end-users gained the ability to control speech attributes such as degree of emotion, rhythm and timbre, along with other suprasegmental features. More ambitious objectives are related to modelling a combination of speakers and languages, e.g. to enable cross-speaker language transfer. Though, no prior work has been done on the more fine-grained analysis of regional accents. To fill this gap, in this thesis we present practical end-to-end solutions to synthesise speech while controlling within-country variations of the same language, and we do so for 6 different dialects of the British Isles. In particular, we first conduct an extensive study of the speaker verification field and tweak state-of-the-art embedding models to work with dialect accents. Then, we adapt standard acoustic models and voice conversion systems by conditioning them on dialect accent representations and finally compare our custom pipelines with a cutting-edge end-to-end architecture from the multi-lingual world. Results show that the adopted models are suitable and have enough capacity to accomplish the task of regional accent conversion. Indeed, we are able to produce speech closely resembling the selected speaker and dialect accent, where the most accurate synthesis is obtained via careful fine-tuning of the multi-lingual model to the multi-dialect case. Finally, we delineate limitations of our multi-stage approach and propose practical mitigations, to be explored in future work

Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR

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

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists

Deep neural network acoustic models for multi-dialect Arabic speech recognition

Speech is a desirable communication method between humans and computers. The major concerns of the automatic speech recognition (ASR) are determining a set of classification features and finding a suitable recognition model for these features. Hidden Markov Models (HMMs) have been demonstrated to be powerful models for representing time varying signals. Artificial Neural Networks (ANNs) have also been widely used for representing time varying quasi-stationary signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used language and is the mother tongue for roughly 200 million people. Arabic speech recognition has been a fertile area of reasearch over the previous two decades, as attested by the various papers that have been published on this subject. This thesis investigates phoneme and acoustic models based on Deep Neural Networks (DNN) and Deep Echo State Networks for multi-dialect Arabic Speech Recognition. Moreover, the TIMIT corpus with a wide variety of American dialects is also aimed to evaluate the proposed models. The availability of speech data that is time-aligned and labelled at phonemic level is a fundamental requirement for building speech recognition systems. A developed Arabic phoneme database (APD) was manually timed and phonetically labelled. This dataset was constructed from the King Abdul-Aziz Arabic Phonetics Database (KAPD) database for Saudi Arabia dialect and the Centre for Spoken Language Understanding (CSLU2002) database for different Arabic dialects. This dataset covers 8148 Arabic phonemes. In addition, a corpus of 120 speakers (13 hours of Arabic speech) randomly selected from the Levantine Arabic dialect database that is used for training and 24 speakers (2.4 hours) for testing are revised and transcription errors were manually corrected. The selected dataset is labelled automatically using the HTK Hidden Markov Model toolkit. TIMIT corpus is also used for phone recognition and acoustic modelling task. We used 462 speakers (3.14 hours) for training and 24 speakers (0.81 hours) for testing. For Automatic Speech Recognition (ASR), a Deep Neural Network (DNN) is used to evaluate its adoption in developing a framewise phoneme recognition and an acoustic modelling system for Arabic speech recognition. Restricted Boltzmann Machines (RBMs) DNN models have not been explored for any Arabic corpora previously. This allows us to claim priority for adopting this RBM DNN model for the Levantine Arabic acoustic models. A post-processing enhancement was also applied to the DNN acoustic model outputs in order to improve the recognition accuracy and to obtain the accuracy at a phoneme level instead of the frame level. This post process has significantly improved the recognition performance. An Echo State Network (ESN) is developed and evaluated for Arabic phoneme recognition with different learning algorithms. This investigated the use of the conventional ESN trained with supervised and forced learning algorithms. A novel combined supervised/forced supervised learning algorithm (unsupervised adaptation) was developed and tested on the proposed optimised Arabic phoneme recognition datasets. This new model is evaluated on the Levantine dataset and empirically compared with the results obtained from the baseline Deep Neural Networks (DNNs). A significant improvement on the recognition performance was achieved when the ESN model was implemented compared to the baseline RBM DNN model’s result. The results show that the ESN model has a better ability for recognizing phonemes sequences than the DNN model for a small vocabulary size dataset. The adoption of the ESNs model for acoustic modeling is seen to be more valid than the adoption of the DNNs model for acoustic modeling speech recognition, as ESNs are recurrent models and expected to support sequence models better than the RBM DNN models even with the contextual input window. The TIMIT corpus is also used to investigate deep learning for framewise phoneme classification and acoustic modelling using Deep Neural Networks (DNNs) and Echo State Networks (ESNs) to allow us to make a direct and valid comparison between the proposed systems investigated in this thesis and the published works in equivalent projects based on framewise phoneme recognition used the TIMIT corpus. Our main finding on this corpus is that ESN network outperform time-windowed RBM DNN ones. However, our developed system ESN-based shows 10% lower performance when it was compared to the other systems recently reported in the literature that used the same corpus. This due to the hardware availability and not applying speaker and noise adaption that can improve the results in this thesis as our aim is to investigate the proposed models for speech recognition and to make a direct comparison between these models