Improving multiple-crowd-sourced transcriptions using a speech recogniser

This paper introduces a method to produce high-quality transcrip- tions of speech data from only two crowd-sourced transcriptions. These transcriptions, produced cheaply by people on the Internet, for example through Amazon Mechanical Turk, are often of low qual- ity. Often, multiple crowd-sourced transcriptions are combined to form one transcription of higher quality. However, the state of the art is to use essentially a form of majority voting, which requires at least three transcriptions for each utterance. This paper shows how to refine this approach to work with only two transcriptions. It then introduces a method that uses a speech recogniser (bootstrapped on a simple combination scheme) to combine transcriptions. When only two crowd-sourced transcriptions are available, on a noisy data set this improves the word error rate to gold-standard transcriptions by 21 % relative.This paper reports on research supported by Cambridge English, University of Cambridge.This is the accepted manuscript of a paper that will be published in the Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. It is currently under an infinite embargo

IMPROVING MULTIPLE-CROWD-SOURCED TRANSCRIPTIONS USING A SPEECH RECOGNISER

ABSTRACT This paper introduces a method to produce high-quality transcriptions of speech data from only two crowd-sourced transcriptions. These transcriptions, produced cheaply by people on the Internet, for example through Amazon Mechanical Turk, are often of low quality. Often, multiple crowd-sourced transcriptions are combined to form one transcription of higher quality. However, the state of the art is to use essentially a form of majority voting, which requires at least three transcriptions for each utterance. This paper shows how to refine this approach to work with only two transcriptions. It then introduces a method that uses a speech recogniser (bootstrapped on a simple combination scheme) to combine transcriptions. When only two crowd-sourced transcriptions are available, on a noisy data set this improves the word error rate to gold-standard transcriptions by 21 % relative

Corpora compilation for prosody-informed speech processing

Research on speech technologies necessitates spoken data, which is usually obtained through read recorded speech, and specifically adapted to the research needs. When the aim is to deal with the prosody involved in speech, the available data must reflect natural and conversational speech, which is usually costly and difficult to get. This paper presents a machine learning-oriented toolkit for collecting, handling, and visualization of speech data, using prosodic heuristic. We present two corpora resulting from these methodologies: PANTED corpus, containing 250 h of English speech from TED Talks, and Heroes corpus containing 8 h of parallel English and Spanish movie speech. We demonstrate their use in two deep learning-based applications: punctuation restoration and machine translation. The presented corpora are freely available to the research community

Crowd-supervised training of spoken language systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-166).Spoken language systems are often deployed with static speech recognizers. Only rarely are parameters in the underlying language, lexical, or acoustic models updated on-the-fly. In the few instances where parameters are learned in an online fashion, developers traditionally resort to unsupervised training techniques, which are known to be inferior to their supervised counterparts. These realities make the development of spoken language interfaces a difficult and somewhat ad-hoc engineering task, since models for each new domain must be built from scratch or adapted from a previous domain. This thesis explores an alternative approach that makes use of human computation to provide crowd-supervised training for spoken language systems. We explore human-in-the-loop algorithms that leverage the collective intelligence of crowds of non-expert individuals to provide valuable training data at a very low cost for actively deployed spoken language systems. We also show that in some domains the crowd can be incentivized to provide training data for free, as a byproduct of interacting with the system itself. Through the automation of crowdsourcing tasks, we construct and demonstrate organic spoken language systems that grow and improve without the aid of an expert. Techniques that rely on collecting data remotely from non-expert users, however, are subject to the problem of noise. This noise can sometimes be heard in audio collected from poor microphones or muddled acoustic environments. Alternatively, noise can take the form of corrupt data from a worker trying to game the system - for example, a paid worker tasked with transcribing audio may leave transcripts blank in hopes of receiving a speedy payment. We develop strategies to mitigate the effects of noise in crowd-collected data and analyze their efficacy. This research spans a number of different application domains of widely-deployed spoken language interfaces, but maintains the common thread of improving the speech recognizer's underlying models with crowd-supervised training algorithms. We experiment with three central components of a speech recognizer: the language model, the lexicon, and the acoustic model. For each component, we demonstrate the utility of a crowd-supervised training framework. For the language model and lexicon, we explicitly show that this framework can be used hands-free, in two organic spoken language systems.by Ian C. McGraw.Ph.D

Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces

Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our goal is to improve the human transcription quality via appropriate user interface design. We focus on iterative interfaces that allow humans to solve tasks based on an initially given suggestion, in this case an automatic transcription. We conduct a user study that reveals considerable quality gains for three variations of iterative interfaces over a non-iterative from-scratch transcription interface. Our iterative interfaces included post-editing, confidence-enhanced post-editing, and a novel retyping interface. All three yielded similar quality on average, but we found that the proposed retyping interface was less sensitive to the difficulty of the segment, and superior when the automatic transcription of the segment contained relatively many errors. An analysis using mixed-effects models allows us to quantify these and other factors and draw conclusions over which interface design should be chosen in which circumstance

Methods for pronunciation assessment in computer aided language learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 149-176).Learning a foreign language is a challenging endeavor that entails acquiring a wide range of new knowledge including words, grammar, gestures, sounds, etc. Mastering these skills all require extensive practice by the learner and opportunities may not always be available. Computer Aided Language Learning (CALL) systems provide non-threatening environments where foreign language skills can be practiced where ever and whenever a student desires. These systems often have several technologies to identify the different types of errors made by a student. This thesis focuses on the problem of identifying mispronunciations made by a foreign language student using a CALL system. We make several assumptions about the nature of the learning activity: it takes place using a dialogue system, it is a task- or game-oriented activity, the student should not be interrupted by the pronunciation feedback system, and that the goal of the feedback system is to identify severe mispronunciations with high reliability. Detecting mispronunciations requires a corpus of speech with human judgements of pronunciation quality. Typical approaches to collecting such a corpus use an expert phonetician to both phonetically transcribe and assign judgements of quality to each phone in a corpus. This is time consuming and expensive. It also places an extra burden on the transcriber. We describe a novel method for obtaining phone level judgements of pronunciation quality by utilizing non-expert, crowd-sourced, word level judgements of pronunciation. Foreign language learners typically exhibit high variation and pronunciation shapes distinct from native speakers that make analysis for mispronunciation difficult. We detail a simple, but effective method for transforming the vowel space of non-native speakers to make mispronunciation detection more robust and accurate. We show that this transformation not only enhances performance on a simple classification task, but also results in distributions that can be better exploited for mispronunciation detection. This transformation of the vowel is exploited to train a mispronunciation detector using a variety of features derived from acoustic model scores and vowel class distributions. We confirm that the transformation technique results in a more robust and accurate identification of mispronunciations than traditional acoustic models.by Mitchell A. Peabody.Ph.D

Automatic Pronunciation Assessment -- A Review

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding

GREC: Multi-domain Speech Recognition for the Greek Language

Μία από τις κορυφαίες προκλήσεις στην Αυτόματη Αναγνώριση Ομιλίας είναι η ανάπτυξη ικανών συστημάτων που μπορούν να έχουν ισχυρή απόδοση μέσα από διαφορετικές συνθήκες ηχογράφησης. Στο παρόν έργο κατασκευάζουμε και αναλύουμε το GREC, μία μεγάλη πολυτομεακή συλλογή δεδομένων για αυτόματη αναγνώριση ομιλίας στην ελληνική γλώσσα. Το GREC αποτελείται από τρεις βάσεις δεδομένων στους θεματικούς τομείς των «εκπομπών ειδήσεων», «ομιλίας από δωρισμένες εγγραφές φωνής», «ηχητικών βιβλίων» και μιας νέας συλλογής δεδομένων στον τομέα των «πολιτικών ομιλιών». Για τη δημιουργία του τελευταίου, συγκεντρώνουμε δεδομένα ομιλίας από ηχογραφήσεις των επίσημων συνεδριάσεων της Βουλής των Ελλήνων, αποδίδοντας ένα σύνολο δεδομένων που αποτελείται από 120 ώρες ομιλίας πολιτικού περιεχομένου. Περιγράφουμε με λεπτομέρεια την καινούρια συλλογή δεδομένων, την προεπεξεργασία και την ευθυγράμμιση ομιλίας, τα οποία βασίζονται στο εργαλείο ανοιχτού λογισμικού Kaldi. Επιπλέον, αξιολογούμε την απόδοση των μοντέλων Gaussian Mixture (GMM) - Hidden Markov (HMM) και Deep Neural Network (DNN) - HMM όταν εφαρμόζονται σε δεδομένα από διαφορετικούς τομείς. Τέλος, προσθέτουμε τη δυνατότητα αυτόματης δεικτοδότησης ομιλητών στο Kaldi-gRPC-Server, ενός εργαλείου γραμμένο σε Python που βασίζεται στο PyKaldi και στο gRPC για βελτιωμένη ανάπτυξη μοντέλων αυτόματης αναγνώρισης ομιλίας.One of the leading challenges in Automatic Speech Recognition (ASR) is the development of robust systems that can perform well under multiple settings. In this work we construct and analyze GREC, a large, multi-domain corpus for automatic speech recognition for the Greek language. GREC is a collection of three available subcorpora over the domains of “news casts”, “crowd-sourced speech”, “audiobooks”, and a new corpus in the domain of “public speeches”. For the creation of the latter, HParl, we collect speech data from recordings of the official proceedings of the Hellenic Parliament, yielding, a dataset which consists of 120 hours of political speech segments. We describe our data collection, pre-processing and alignment setup, which are based on Kaldi toolkit. Furthermore, we perform extensive ablations on the recognition performance of Gaussian Mixture (GMM) - Hidden Markov (HMM) models and Deep Neural Network (DNN) - HMM models over the different domains. Finally, we integrate speaker diarization features to Kaldi-gRPC-Server, a modern, pythonic tool based on PyKaldi and gRPC for streamlined deployment of Kaldi based speech recognition

Recognizing emotions in spoken dialogue with acoustic and lexical cues

Automatic emotion recognition has long been a focus of Affective Computing. It has become increasingly apparent that awareness of human emotions in Human-Computer Interaction (HCI) is crucial for advancing related technologies, such as dialogue systems. However, performance of current automatic emotion recognition is disappointing compared to human performance. Current research on emotion recognition in spoken dialogue focuses on identifying better feature representations and recognition models from a data-driven point of view. The goal of this thesis is to explore how incorporating prior knowledge of human emotion recognition in the automatic model can improve state-of-the-art performance of automatic emotion recognition in spoken dialogue. Specifically, we study this by proposing knowledge-inspired features representing occurrences of disfluency and non-verbal vocalisation in speech, and by building a multimodal recognition model that combines acoustic and lexical features in a knowledge-inspired hierarchical structure. In our study, emotions are represented with the Arousal, Expectancy, Power, and Valence emotion dimensions. We build unimodal and multimodal emotion recognition models to study the proposed features and modelling approach, and perform emotion recognition on both spontaneous and acted dialogue. Psycholinguistic studies have suggested that DISfluency and Non-verbal Vocalisation (DIS-NV) in dialogue is related to emotions. However, these affective cues in spoken dialogue are overlooked by current automatic emotion recognition research. Thus, we propose features for recognizing emotions in spoken dialogue which describe five types of DIS-NV in utterances, namely filled pause, filler, stutter, laughter, and audible breath. Our experiments show that this small set of features is predictive of emotions. Our DIS-NV features achieve better performance than benchmark acoustic and lexical features for recognizing all emotion dimensions in spontaneous dialogue. Consistent with Psycholinguistic studies, the DIS-NV features are especially predictive of the Expectancy dimension of emotion, which relates to speaker uncertainty. Our study illustrates the relationship between DIS-NVs and emotions in dialogue, which contributes to Psycholinguistic understanding of them as well. Note that our DIS-NV features are based on manual annotations, yet our long-term goal is to apply our emotion recognition model to HCI systems. Thus, we conduct preliminary experiments on automatic detection of DIS-NVs, and on using automatically detected DIS-NV features for emotion recognition. Our results show that DIS-NVs can be automatically detected from speech with stable accuracy, and auto-detected DIS-NV features remain predictive of emotions in spontaneous dialogue. This suggests that our emotion recognition model can be applied to a fully automatic system in the future, and holds the potential to improve the quality of emotional interaction in current HCI systems. To study the robustness of the DIS-NV features, we conduct cross-corpora experiments on both spontaneous and acted dialogue. We identify how dialogue type influences the performance of DIS-NV features and emotion recognition models. DIS-NVs contain additional information beyond acoustic characteristics or lexical contents. Thus, we study the gain of modality fusion for emotion recognition with the DIS-NV features. Previous work combines different feature sets by fusing modalities at the same level using two types of fusion strategies: Feature-Level (FL) fusion, which concatenates feature sets before recognition; and Decision-Level (DL) fusion, which makes the final decision based on outputs of all unimodal models. However, features from different modalities may describe data at different time scales or levels of abstraction. Moreover, Cognitive Science research indicates that when perceiving emotions, humans make use of information from different modalities at different cognitive levels and time steps. Therefore, we propose a HierarchicaL (HL) fusion strategy for multimodal emotion recognition, which incorporates features that describe data at a longer time interval or which are more abstract at higher levels of its knowledge-inspired hierarchy. Compared to FL and DL fusion, HL fusion incorporates both inter- and intra-modality differences. Our experiments show that HL fusion consistently outperforms FL and DL fusion on multimodal emotion recognition in both spontaneous and acted dialogue. The HL model combining our DIS-NV features with benchmark acoustic and lexical features improves current performance of multimodal emotion recognition in spoken dialogue. To study how other emotion-related tasks of spoken dialogue can benefit from the proposed approaches, we apply the DIS-NV features and the HL fusion strategy to recognize movie-induced emotions. Our experiments show that although designed for recognizing emotions in spoken dialogue, DIS-NV features and HL fusion remain effective for recognizing movie-induced emotions. This suggests that other emotion-related tasks can also benefit from the proposed features and model structure