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Multi-task Learning for Speaker Verification and Voice Trigger Detection

arXiv.org Machine Learning

Automatic speech transcription and speaker recognition are usually treated as separate tasks even though they are interdependent. In this study, we investigate training a single network to perform both tasks jointly. We train the network in a supervised multi-task learning setup, where the speech transcription branch of the network is trained to minimise a phonetic connectionist temporal classification (CTC) loss while the speaker recognition branch of the network is trained to label the input sequence with the correct label for the speaker. We present a large-scale empirical study where the model is trained using several thousand hours of labelled training data for each task. We evaluate the speech transcription branch of the network on a voice trigger detection task while the speaker recognition branch is evaluated on a speaker verification task. Results demonstrate that the network is able to encode both phonetic \emph{and} speaker information in its learnt representations while yielding accuracies at least as good as the baseline models for each task, with the same number of parameters as the independent models.


Apple details AI to help voice assistants recognize hotwords and multilingual speakers

#artificialintelligence

Speech recognition is an acute area of interest for Apple, whose cross-platform Siri virtual assistant is used by over 500 million customers worldwide. This past week, the tech giant published a series of preprint research papers investigating techniques to improve voice trigger detection and speaker verification, as well as language identification for multiple speakers. In the first of the papers, a team of Apple researchers propose an AI model trained to perform both the task of automatic speech recognition and speaker recognition. As they explain in the abstract, the commands recognized by speech-based personal assistants are usually prefixed with a trigger phrase (e.g., "Hey, Siri"), and detecting this trigger phrase involves two steps. The AI first must decide whether the phonetic content in the input audio matches that of the trigger phrase (voice trigger detection), and then it must determine whether the speaker's voice matches the voice of a registered user or users (speaker verification).


Multi-task Learning for Voice Trigger Detection

arXiv.org Machine Learning

We describe the design of a voice trigger detection system for smart speakers. In this study, we address two major challenges. The first is that the detectors are deployed in complex acoustic environments with external noise and loud playback by the device itself. Secondly, collecting training examples for a specific keyword or trigger phrase is challenging resulting in a scarcity of trigger phrase specific training data. We describe a two-stage cascaded architecture where a low-power detector is always running and listening for the trigger phrase. If a detection is made at this stage, the candidate audio segment is re-scored by larger, more complex models to verify that the segment contains the trigger phrase. In this study, we focus our attention on the architecture and design of these second-pass detectors. We start by training a general acoustic model that produces phonetic transcriptions given a large labelled training dataset. Next, we collect a much smaller dataset of examples that are challenging for the baseline system. We then use multi-task learning to train a model to simultaneously produce accurate phonetic transcriptions on the larger dataset \emph{and} discriminate between true and easily confusable examples using the smaller dataset. Our results demonstrate that the proposed model reduces errors by half compared to the baseline in a range of challenging test conditions \emph{without} requiring extra parameters.


Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages

arXiv.org Artificial Intelligence

With recent advancements in language technologies, humansare now interacting with technology through speech. To in-crease the reach of these technologies, we need to build suchsystems in local languages. A major bottleneck here are theunderlying data-intensive parts that make up such systems,including automatic speech recognition (ASR) systems thatrequire large amounts of labelled data. With the aim of aidingdevelopment of dialog systems in low resourced languages,we propose a novel acoustics based intent recognition systemthat uses discovered phonetic units for intent classification.The system is made up of two blocks - the first block gen-erates a transcript of discovered phonetic units for the inputaudio, and the second block which performs intent classifi-cation from the generated phonemic transcripts. Our workpresents results for such a system for two languages families- Indic languages and Romance languages, for two differentintent recognition tasks. We also perform multilingual train-ing of our intent classifier and show improved cross-lingualtransfer and performance on an unknown language with zeroresources in the same language family.


VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking

arXiv.org Machine Learning

ABSTRACT In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals. Index Terms-- Source separation, speaker recognition, spectrogram masking, speech recognition 1. INTRODUCTION Recent advances in speech recognition have led to performance improvement in challenging scenarios such as noisy and far-field conditions. However, speech recognition systems still perform poorly when the speaker of interest is recorded in crowded environments, i.e., with interfering speakers in the foreground or background. One way to deal with this issue is to first apply a speech separation system on the noisy audio in order to separate the voices from different speakers.