Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

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. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Semi-Supervised Acoustic Model Training by Discriminative Data Selection from Multiple ASR Systems' Hypotheses

While the performance of ASR systems depends on the size of the training data, it is very costly to prepare accurate and faithful transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In the proposed method, we obtain ASR hypotheses by complementary GMM-and DNN-based ASR systems. Then, a set of CRF-based classifiers is trained to select the correct hypotheses and verify the selected data. The proposed hypothesis combination shows higher quality compared with the conventional system combination method (ROVER). Moreover, compared with the conventional data selection based on confidence measure score, our method is demonstrated more effective for filtering usable data. Significant improvement in the ASR accuracy is achieved over the baseline system and in comparison with the models trained with the conventional system combination and data selection methods

Sequence Teacher-Student Training of Acoustic Models for Automatic Free Speaking Language Assessment

A high performance automatic speech recognition (ASR) system is an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system is required to be capable of recognising non-native spontaneous English speech and to be deployable under real-time conditions. The performance of ASR systems can often be significantly improved by leveraging upon multiple systems that are complementary, such as an ensemble. Ensemble methods, however, can be computationally expensive, often requiring multiple decoding runs, which makes them impractical for deployment. In this paper, a lattice-free implementation of sequence-level teacher-student training is used to reduce this computational cost, thereby allowing for real-time applications. This method allows a single student model to emulate the performance of an ensemble of teachers, but without the need for multiple decoding runs. Adaptations of the student model to speakers from different first languages (L1s) and grades are also explored.Cambridge Assessment Englis

Automatic speech recognition system development in the “wild“

The standard framework for developing an automatic speech recognition (ASR) system is to generate training and development data for building the system, and evaluation data for the final performance analysis. All the data is assumed to come from the domain of interest. Though this framework is matched to some tasks, it is more challenging for systems that are required to operate over broad domains, or where the ability to collect the required data is limited. This paper discusses ASR work performed under the IARPA MATERIAL program, which is aimed at cross-language information retrieval, and examines this challenging scenario. In terms of available data, only limited narrow-band conversational telephone speech data was provided. However, the system is required to operate over a range of domains, including broadcast data. As no data is available for the broadcast domain, this paper proposes an approach for system development based on scraping "related" data from the web, and using ASR system confidence scores as the primary metric for developing the acoustic and language model components. As an initial evaluation of the approach, the Swahili development language is used, with the final system performance assessed on the IARPA MATERIAL Analysis Pack 1 data.The Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via Air Force Research Laboratory (AFRL