10 research outputs found
Lessons from Building Acoustic Models with a Million Hours of Speech
This is a report of our lessons learned building acoustic models from 1
Million hours of unlabeled speech, while labeled speech is restricted to 7,000
hours. We employ student/teacher training on unlabeled data, helping scale out
target generation in comparison to confidence model based methods, which
require a decoder and a confidence model. To optimize storage and to
parallelize target generation, we store high valued logits from the teacher
model. Introducing the notion of scheduled learning, we interleave learning on
unlabeled and labeled data. To scale distributed training across a large number
of GPUs, we use BMUF with 64 GPUs, while performing sequence training only on
labeled data with gradient threshold compression SGD using 16 GPUs. Our
experiments show that extremely large amounts of data are indeed useful; with
little hyper-parameter tuning, we obtain relative WER improvements in the 10 to
20% range, with higher gains in noisier conditions.Comment: "Copyright 2019 IEEE. Personal use of this material is permitted.
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Anchored Speech Recognition with Neural Transducers
Neural transducers have achieved human level performance on standard speech
recognition benchmarks. However, their performance significantly degrades in
the presence of cross-talk, especially when the primary speaker has a low
signal-to-noise ratio. Anchored speech recognition refers to a class of methods
that use information from an anchor segment (e.g., wake-words) to recognize
device-directed speech while ignoring interfering background speech. In this
paper, we investigate anchored speech recognition to make neural transducers
robust to background speech. We extract context information from the anchor
segment with a tiny auxiliary network, and use encoder biasing and joiner
gating to guide the transducer towards the target speech. Moreover, to improve
the robustness of context embedding extraction, we propose auxiliary training
objectives to disentangle lexical content from speaking style. We evaluate our
methods on synthetic LibriSpeech-based mixtures comprising several SNR and
overlap conditions; they improve relative word error rates by 19.6% over a
strong baseline, when averaged over all conditions.Comment: To appear at IEEE ICASSP 202
Development of an anthropomorphic mobile manipulator with human, machine and environment interaction
An anthropomorphic mobile manipulator robot (CHARMIE) is being developed by the University of Minho's Automation and Robotics Laboratory (LAR). The robot gathers sensorial information and processes using neural networks, actuating in real time. The robot's two arms allow object and machine interaction. Its anthropomorphic structure is advantageous since machines are designed and optimized for human interaction. Sound output allows it to relay information to workers and provide feedback. Allying these features with communication with a database or remote operator results in establishment of a bridge between the physical environment and virtual domain. The goal is an increase in information flow and accessibility. This paper presents the current state of the project, intended features and how it can contribute to the development of Industry 4.0. Focus is given to already finished work, detailing the methodology used for two of the robot's subsystems: locomotion system; lower limbs of the robot.- This project has been supported by the ALGORITMI Research Centre of University of Minho's School of Engineering
WAKE WORD DETECTION AND ITS APPLICATIONS
Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. Novel methods are proposed to train a wake word detection system from partially labeled training data, and to use it in on-line applications. In the system, the prerequisite of frame-level alignment is removed, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word. Also, an FST-based decoder is presented to perform online detection. The suite of methods greatly improve the wake word detection performance across several datasets.
A novel neural network for acoustic modeling in wake word detection is also investigated. Specifically, the performance of several variants of chunk-wise streaming Transformers tailored for wake word detection is explored, including looking-ahead to the next chunk, gradient stopping, different positional embedding methods and adding same-layer dependency between chunks. Experiments demonstrate that the proposed Transformer model outperforms the baseline convolutional network significantly with a comparable model size, while still maintaining linear complexity w.r.t. the input length.
For the application of the detected wake word in ASR, the problem of improving speech recognition with the help of the detected wake word is investigated. Voice-controlled house-hold devices face the difficulty of performing speech recognition of device-directed speech in the presence of interfering background speech. Two end-to-end models are proposed to tackle this problem with information extracted from the anchored segment. The anchored segment refers to the wake word segment of the audio stream, which contains valuable speaker information that can be used to suppress interfering speech and background noise. A multi-task learning setup is also explored where the ideal mask, obtained from a data synthesis procedure, is used to guide the model training. In addition, a way to synthesize "noisy" speech from "clean" speech is also proposed to mitigate the mismatch between training and test data. The proposed methods show large word error reduction for Amazon Alexa live data with interfering background speech, without sacrificing the performance on clean speech