Streaming keyword spotting on mobile devices

In this work we explore the latency and accuracy of keyword spotting (KWS) models in streaming and non-streaming modes on mobile phones. NN model conversion from non-streaming mode (model receives the whole input sequence and then returns the classification result) to streaming mode (model receives portion of the input sequence and classifies it incrementally) may require manual model rewriting. We address this by designing a Tensorflow/Keras based library which allows automatic conversion of non-streaming models to streaming ones with minimum effort. With this library we benchmark multiple KWS models in both streaming and non-streaming modes on mobile phones and demonstrate different tradeoffs between latency and accuracy. We also explore novel KWS models with multi-head attention which reduce the classification error over the state-of-art by 10% on Google speech commands data sets V2. The streaming library with all experiments is open-sourced

Data-Efficient Methods for Dialogue Systems

Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa or business-oriented solutions. Deep learning underlies many recent breakthroughs in dialogue systems but requires very large amounts of training data, often annotated by experts. Trained with smaller data, these methods end up severely lacking robustness (e.g. to disfluencies and out-of-domain input), and often just have too little generalisation power. In this thesis, we address the above issues by introducing a series of methods for training robust dialogue systems from minimal data. Firstly, we study two orthogonal approaches to dialogue: linguistically informed and machine learning-based - from the data efficiency perspective. We outline the steps to obtain data-efficient solutions with either approach. We then introduce two data-efficient models for dialogue response generation: the Dialogue Knowledge Transfer Network based on latent variable dialogue representations, and the hybrid Generative-Retrieval Transformer model (ranked first at the DSTC 8 Fast Domain Adaptation task). Next, we address the problem of robustness given minimal data. As such, propose a multitask LSTM-based model for domain-general disfluency detection. For the problem of out-of-domain input, we present Turn Dropout, a data augmentation technique for anomaly detection only using in-domain data, and introduce autoencoder-augmented models for efficient training with Turn Dropout. Finally, we focus on social dialogue and introduce a neural model for response ranking in social conversation used in Alana, the 3rd place winner in the Amazon Alexa Prize 2017 and 2018. We employ a novel technique of predicting the dialogue length as the main ranking objective and show that this approach improves upon the ratings-based counterpart in terms of data efficiency while matching it in performance.Comment: PhD thesis submitted at Heriot-Watt University. Contains previously published work (see the list in Section 1.4