English Broadcast News Speech Recognition by Humans and Machines

With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversational telephone speech (CTS) recognition. In this paper we evaluate the usefulness of these proposed techniques on broadcast news (BN), a similar challenging task. We also perform a set of recognition measurements to understand how close the achieved automatic speech recognition results are to human performance on this task. On two publicly available BN test sets, DEV04F and RT04, our speech recognition system using LSTM and residual network based acoustic models with a combination of n-gram and neural network language models performs at 6.5% and 5.9% word error rate. By achieving new performance milestones on these test sets, our experiments show that techniques developed on other related tasks, like CTS, can be transferred to achieve similar performance. In contrast, the best measured human recognition performance on these test sets is much lower, at 3.6% and 2.8% respectively, indicating that there is still room for new techniques and improvements in this space, to reach human performance levels.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 work

Comparing Human and Machine Errors in Conversational Speech Transcription

Recent work in automatic recognition of conversational telephone speech (CTS) has achieved accuracy levels comparable to human transcribers, although there is some debate how to precisely quantify human performance on this task, using the NIST 2000 CTS evaluation set. This raises the question what systematic differences, if any, may be found differentiating human from machine transcription errors. In this paper we approach this question by comparing the output of our most accurate CTS recognition system to that of a standard speech transcription vendor pipeline. We find that the most frequent substitution, deletion and insertion error types of both outputs show a high degree of overlap. The only notable exception is that the automatic recognizer tends to confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans tend not to make this error, presumably due to the distinctive and opposing pragmatic functions attached to these words. Furthermore, we quantify the correlation between human and machine errors at the speaker level, and investigate the effect of speaker overlap between training and test data. Finally, we report on an informal "Turing test" asking humans to discriminate between automatic and human transcription error cases

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

Cross-domain paraphrasing for improving language modelling using out-of-domain data

In natural languages the variability in the underlying linguistic generation rules significantly alters the observed surface word sequence they create, and thus introduces a mismatch against other data generated via alternative realizations associated with, for example, a different domain. Hence, direct modelling of out-of-domain data can result in poor generalization to the indomain data of interest. To handle this problem, this paper investigated using cross-domain paraphrastic language models to improve in-domain language modelling (LM) using out-ofdomain data. Phrase level paraphrase models learnt from each domain were used to generate paraphrase variants for the data of other domains. These were used to both improve the context coverage of in-domain data, and reduce the domain mismatch of the out-of-domain data. Significant error rate reduction of 0.6% absolute was obtained on a state-of-the-art conversational telephone speech recognition task using a cross-domain paraphrastic multi-level LM trained on a billion words of mixed conversational and broadcast news data. Consistent improvements on the in-domain data context coverage were also obtained.The research leading to these results was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology) and DARPA under the Broad Operational Language Translation (BOLT) program.This is the accepted manuscript. The final version is available at http://www.isca-speech.org/archive/interspeech_2013/i13_3424.htm

Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems

Fundamental modelling differences between hybrid and end-to-end (E2E) automatic speech recognition (ASR) systems create large diversity and complementarity among them. This paper investigates multi-pass rescoring and cross adaptation based system combination approaches for hybrid TDNN and Conformer E2E ASR systems. In multi-pass rescoring, state-of-the-art hybrid LF-MMI trained CNN-TDNN system featuring speed perturbation, SpecAugment and Bayesian learning hidden unit contributions (LHUC) speaker adaptation was used to produce initial N-best outputs before being rescored by the speaker adapted Conformer system using a 2-way cross system score interpolation. In cross adaptation, the hybrid CNN-TDNN system was adapted to the 1-best output of the Conformer system or vice versa. Experiments on the 300-hour Switchboard corpus suggest that the combined systems derived using either of the two system combination approaches outperformed the individual systems. The best combined system obtained using multi-pass rescoring produced statistically significant word error rate (WER) reductions of 2.5% to 3.9% absolute (22.5% to 28.9% relative) over the stand alone Conformer system on the NIST Hub5'00, Rt03 and Rt02 evaluation data.Comment: It' s accepted to ISCA 202

Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates

International audienceDespite years of speech recognition research, little is known about which words tend to be misrecognized and why. Previous work has shown that errors increase for infrequent words, short words, and very loud or fast speech, but many other presumed causes of error (e.g., nearby disfluencies, turn-initial words, phonetic neighborhood density) have never been carefully tested. The reasons for the huge differences found in error rates between speakers also remain largely mysterious. Using a mixed-effects regression model, we investigate these and other factors by analyzing the errors of two state-of-the-art recognizers on conversational speech. Words with higher error rates include those with extreme prosodic characteristics, those occurring turn-initially or as discourse markers, and : acoustically similar words that also have similar language model probabilities. Words preceding disfluent interruption points (first repetition tokens and words before fragments) also have higher error rates. Finally, even after accounting for other factors, speaker differences cause enormous variance in error rates, suggesting that speaker error rate variance is not fully explained by differences in word choice, fluency, or prosodic characteristics. We also propose that doubly confusable pairs, rather than high neighborhood density, may better explain phonetic neighborhood errors in human speech processing

Automatic transcription of multi-genre media archives

This paper describes some recent results of our collaborative work on developing a speech recognition system for the automatic transcription or media archives from the British Broadcasting Corporation (BBC). The material includes a wide diversity of shows with their associated metadata. The latter are highly diverse in terms of completeness, reliability and accuracy. First, we investigate how to improve lightly supervised acoustic training, when timestamp information is inaccurate and when speech deviates significantly from the transcription, and how to perform evaluations when no reference transcripts are available. An automatic timestamp correction method as well as a word and segment level combination approaches between the lightly supervised transcripts and the original programme scripts are presented which yield improved metadata. Experimental results show that systems trained using the improved metadata consistently outperform those trained with only the original lightly supervised decoding hypotheses. Secondly, we show that the recognition task may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we describe Multi-level Adaptive Networks, a novel technique for incorporating information from out-of domain posterior features using deep neural network. We show that it provides a substantial reduction in WER over other systems including a PLP-based baseline, in-domain tandem features, and the best out-of-domain tandem features.This research was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology).This paper was presented at the First Workshop on Speech, Language and Audio in Multimedia, August 22-23, 2013; Marseille. It was published in CEUR Workshop Proceedings at http://ceur-ws.org/Vol-1012/

Effects of Transcription Errors on Supervised Learning in Speech Recognition

Supervised learning using Hidden Markov Models has been used to train acoustic models for automatic speech recognition for several years. Typically clean transcriptions form the basis for this training regimen. However, results have shown that using sources of readily available transcriptions, which can be erroneous at times (e.g., closed captions) do not degrade the performance significantly. This work analyzes the effects of mislabeled data on recognition accuracy. For this purpose, the training is performed using manually corrupted training data and the results are observed on three different databases: TIDigits, Alphadigits and SwitchBoard. For Alphadigits, with 16% of data mislabeled, the performance of the system degrades by 12% relative to the baseline results. For a complex task like SWITCHBOARD, at 16% mislabeled training data, the performance of the system degrades by 8.5% relative to the baseline results. The training process is more robust to mislabeled data because the Gaussian mixtures that are used to model the underlying distribution tend to cluster around the majority of the correct data. The outliers (incorrect data) do not contribute significantly to the reestimation process