AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline

An open-source Mandarin speech corpus called AISHELL-1 is released. It is by far the largest corpus which is suitable for conducting the speech recognition research and building speech recognition systems for Mandarin. The recording procedure, including audio capturing devices and environments are presented in details. The preparation of the related resources, including transcriptions and lexicon are described. The corpus is released with a Kaldi recipe. Experimental results implies that the quality of audio recordings and transcriptions are promising.Comment: Oriental COCOSDA 201

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Performance analysis and optimization of automatic speech recognition

© 2018 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.Fast and accurate Automatic Speech Recognition (ASR) is emerging as a key application for mobile devices. Delivering ASR on such devices is challenging due to the compute-intensive nature of the problem and the power constraints of embedded systems. In this paper, we provide a performance and energy characterization of Pocketsphinx, a popular toolset for ASR that targets mobile devices. We identify the computation of the Gaussian Mixture Model (GMM) as the main bottleneck, consuming more than 80 percent of the execution time. The CPI stack analysis shows that branches and main memory accesses are the main performance limiting factors for GMM computation. We propose several software-level optimizations driven by the power/performance analysis. Unlike previous proposals that trade accuracy for performance by reducing the number of Gaussians evaluated, we maintain accuracy and improve performance by effectively using the underlying CPU microarchitecture. First, we use a refactored implementation of the innermost loop of the GMM evaluation code to ameliorate the impact of branches. Second, we exploit the vector unit available on most modern CPUs to boost GMM computation, introducing a novel memory layout for storing the means and variances of the Gaussians in order to maximize the effectiveness of vectorization. Third, we compute the Gaussians for multiple frames in parallel, so means and variances can be fetched once in the on-chip caches and reused across multiple frames, significantly reducing memory bandwidth usage. We evaluate our optimizations using both hardware counters on real CPUs and simulations. Our experimental results show that the proposed optimizations provide 2.68x speedup over the baseline Pocketsphinx decoder on a high-end Intel Skylake CPU, while achieving 61 percent energy savings. On a modern ARM Cortex-A57 mobile processor our techniques improve performance by 1.85x, while providing 59 percent energy savings without any loss in the accuracy of the ASR system.Peer ReviewedPostprint (author's final draft

Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed

Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It has a wide range of multimedia applications such as in surveillance, Internet telephony, and as an aid to a person with hearing impairments. However, most of the work in speechreading has been limited to text generation from silent videos. Recently, research has started venturing into generating (audio) speech from silent video sequences but there have been no developments thus far in dealing with divergent views and poses of a speaker. Thus although, we have multiple camera feeds for the speech of a user, but we have failed in using these multiple video feeds for dealing with the different poses. To this end, this paper presents the world's first ever multi-view speech reading and reconstruction system. This work encompasses the boundaries of multimedia research by putting forth a model which leverages silent video feeds from multiple cameras recording the same subject to generate intelligent speech for a speaker. Initial results confirm the usefulness of exploiting multiple camera views in building an efficient speech reading and reconstruction system. It further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Next, it lays out various innovative applications for the proposed system focusing on its potential prodigious impact in not just security arena but in many other multimedia analytics problems.Comment: 2018 ACM Multimedia Conference (MM '18), October 22--26, 2018, Seoul, Republic of Kore

An ultra low-power hardware accelerator for automatic speech recognition

Automatic Speech Recognition (ASR) is becoming increasingly ubiquitous, especially in the mobile segment. Fast and accurate ASR comes at a high energy cost which is not affordable for the tiny power budget of mobile devices. Hardware acceleration can reduce power consumption of ASR systems, while delivering high-performance. In this paper, we present an accelerator for large-vocabulary, speaker-independent, continuous speech recognition. It focuses on the Viterbi search algorithm, that represents the main bottleneck in an ASR system. The proposed design includes innovative techniques to improve the memory subsystem, since memory is identified as the main bottleneck for performance and power in the design of these accelerators. We propose a prefetching scheme tailored to the needs of an ASR system that hides main memory latency for a large fraction of the memory accesses with a negligible impact on area. In addition, we introduce a novel bandwidth saving technique that removes 20% of the off-chip memory accesses issued during the Viterbi search. The proposed design outperforms software implementations running on the CPU by orders of magnitude and achieves 1.7x speedup over a highly optimized CUDA implementation running on a high-end Geforce GTX 980 GPU, while reducing by two orders of magnitude (287x) the energy required to convert the speech into text.Peer ReviewedPostprint (author's final draft

Current trends in multilingual speech processing

In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin

Confidence Scoring and Speaker Adaptation in Mobile Automatic Speech Recognition Applications

Generally, the user group of a language is remarkably diverse in terms of speaker-specific characteristics such as dialect and speaking style. Hence, quality of spoken content varies notably from one individual to another. This diversity causes problems for Automatic Speech Recognition systems. An Automatic Speech Recognition system should be able to assess the hypothesised results. This can be done by evaluating a confidence measure on the recognition results and comparing the resulting measure to a specified threshold. This threshold value, referred to as confidence score, informs how reliable a particular recognition result is for the given speech. A system should perform optimally irrespective of input speaker characteristics. However, most systems are inflexible and non-adaptive and thus, speaker adaptability can be improved. For achieving these purposes, a solid criterion is required to evaluate the quality of spoken content and the system should be made robust and adaptive towards new speakers as well. This thesis implements a confidence score using posterior probabilities to examine the quality of the output, based on the speech data and corpora provided by Devoca Oy. Furthermore, speaker adaptation algorithms: Maximum Likelihood Linear Regression and Maximum a Posteriori are applied on a GMM-HMM system and their results are compared. Experiments show that Maximum a Posteriori adaptation brings 2% to 25% improvement in word error rates of semi-continuous model and is recommended for use in the commercial product. The results of other methods are also reported. In addition, word graph is suggested as the method for obtaining posterior probabilities. Since it guarantees no such improvement in the results, the confidence score is proposed as an optional feature for the system