Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training

Self-imitating feedback is an effective and learner-friendly method for non-native learners in Computer-Assisted Pronunciation Training. Acoustic characteristics in native utterances are extracted and transplanted onto learner's own speech input, and given back to the learner as a corrective feedback. Previous works focused on speech conversion using prosodic transplantation techniques based on PSOLA algorithm. Motivated by the visual differences found in spectrograms of native and non-native speeches, we investigated applying GAN to generate self-imitating feedback by utilizing generator's ability through adversarial training. Because this mapping is highly under-constrained, we also adopt cycle consistency loss to encourage the output to preserve the global structure, which is shared by native and non-native utterances. Trained on 97,200 spectrogram images of short utterances produced by native and non-native speakers of Korean, the generator is able to successfully transform the non-native spectrogram input to a spectrogram with properties of self-imitating feedback. Furthermore, the transformed spectrogram shows segmental corrections that cannot be obtained by prosodic transplantation. Perceptual test comparing the self-imitating and correcting abilities of our method with the baseline PSOLA method shows that the generative approach with cycle consistency loss is promising

Learning Fault-tolerant Speech Parsing with SCREEN

This paper describes a new approach and a system SCREEN for fault-tolerant speech parsing. SCREEEN stands for Symbolic Connectionist Robust EnterprisE for Natural language. Speech parsing describes the syntactic and semantic analysis of spontaneous spoken language. The general approach is based on incremental immediate flat analysis, learning of syntactic and semantic speech parsing, parallel integration of current hypotheses, and the consideration of various forms of speech related errors. The goal for this approach is to explore the parallel interactions between various knowledge sources for learning incremental fault-tolerant speech parsing. This approach is examined in a system SCREEN using various hybrid connectionist techniques. Hybrid connectionist techniques are examined because of their promising properties of inherent fault tolerance, learning, gradedness and parallel constraint integration. The input for SCREEN is hypotheses about recognized words of a spoken utterance potentially analyzed by a speech system, the output is hypotheses about the flat syntactic and semantic analysis of the utterance. In this paper we focus on the general approach, the overall architecture, and examples for learning flat syntactic speech parsing. Different from most other speech language architectures SCREEN emphasizes an interactive rather than an autonomous position, learning rather than encoding, flat analysis rather than in-depth analysis, and fault-tolerant processing of phonetic, syntactic and semantic knowledge.Comment: 6 pages, postscript, compressed, uuencoded to appear in Proceedings of AAAI 9

Deconstructing comprehensibility: identifying the linguistic influences on listeners' L2 comprehensibility ratings

Comprehensibility, a major concept in second language (L2) pronunciation research that denotes listeners’ perceptions of how easily they understand L2 speech, is central to interlocutors’ communicative success in real-world contexts. Although comprehensibility has been modeled in several L2 oral proficiency scales—for example, the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS)—shortcomings of existing scales (e.g., vague descriptors) reflect limited empirical evidence as to which linguistic aspects influence listeners’ judgments of L2 comprehensibility at different ability levels. To address this gap, a mixed-methods approach was used in the present study to gain a deeper understanding of the linguistic aspects underlying listeners’ L2 comprehensibility ratings. First, speech samples of 40 native French learners of English were analyzed using 19 quantitative speech measures, including segmental, suprasegmental, fluency, lexical, grammatical, and discourse-level variables. These measures were then correlated with 60 native English listeners’ scalar judgments of the speakers’ comprehensibility. Next, three English as a second language (ESL) teachers provided introspective reports on the linguistic aspects of speech that they attended to when judging L2 comprehensibility. Following data triangulation, five speech measures were identified that clearly distinguished between L2 learners at different comprehensibility levels. Lexical richness and fluency measures differentiated between low-level learners; grammatical and discourse-level measures differentiated between high-level learners; and word stress errors discriminated between learners of all levels

Fluency in dialogue: Turn‐taking behavior shapes perceived fluency in native and nonnative speech

Fluency is an important part of research on second language learning, but most research on language proficiency typically has not included oral fluency as part of interaction, even though natural communication usually occurs in conversations. The present study considered aspects of turn-taking behavior as part of the construct of fluency and investigated whether these aspects differentially influence perceived fluency ratings of native and non-native speech. Results from two experiments using acoustically manipulated speech showed that, in native speech, too ‘eager’ (interrupting a question with a fast answer) and too ‘reluctant’ answers (answering slowly after a long turn gap) negatively affected fluency ratings. However, in non-native speech, only too ‘reluctant’ answers led to lower fluency ratings. Thus, we demonstrate that acoustic properties of dialogue are perceived as part of fluency. By adding to our current understanding of dialogue fluency, these lab-based findings carry implications for language teaching and assessmen

Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena

Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, and self-corrections. But training data for machine learning approaches to dialogue processing is often either cleaned-up or wholly synthetic in order to avoid such phenomena. The question then arises of how well systems trained on such clean data generalise to real spontaneous dialogue, or indeed whether they are trainable at all on naturally occurring dialogue data. To answer this question, we created a new corpus called bAbI+ by systematically adding natural spontaneous incremental dialogue phenomena such as restarts and self-corrections to the Facebook AI Research's bAbI dialogues dataset. We then explore the performance of a state-of-the-art retrieval model, MemN2N, on this more natural dataset. Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so. Finally, we go on to show that an incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both bAbI and bAbI+, highlighting the generalisation properties of linguistically informed dialogue models.Comment: 9 pages, 3 figures, 2 tables. Accepted as a full paper for SemDial 201

Speech monitoring and phonologically-mediated eye gaze in language perception and production: a comparison using printed word eye-tracking

The Perceptual Loop Theory of speech monitoring assumes that speakers routinely inspect their inner speech. In contrast, Huettig and Hartsuiker (2010) observed that listening to one's own speech during language production drives eye-movements to phonologically related printed words with a similar time-course as listening to someone else's speech does in speech perception experiments. This suggests that speakers use their speech perception system to listen to their own overt speech, but not to their inner speech. However, a direct comparison between production and perception with the same stimuli and participants is lacking so far. The current printed word eye-tracking experiment therefore used a within-subjects design, combining production and perception. Displays showed four words, of which one, the target, either had to be named or was presented auditorily. Accompanying words were phonologically related, semantically related, or unrelated to the target. There were small increases in looks to phonological competitors with a similar time-course in both production and perception. Phonological effects in perception however lasted longer and had a much larger magnitude. We conjecture that this difference is related to a difference in predictability of one's own and someone else's speech, which in turn has consequences for lexical competition in other-perception and possibly suppression of activation in self-perception

The Speech-Language Interface in the Spoken Language Translator

The Spoken Language Translator is a prototype for practically useful systems capable of translating continuous spoken language within restricted domains. The prototype system translates air travel (ATIS) queries from spoken English to spoken Swedish and to French. It is constructed, with as few modifications as possible, from existing pieces of speech and language processing software. The speech recognizer and language understander are connected by a fairly conventional pipelined N-best interface. This paper focuses on the ways in which the language processor makes intelligent use of the sentence hypotheses delivered by the recognizer. These ways include (1) producing modified hypotheses to reflect the possible presence of repairs in the uttered word sequence; (2) fast parsing with a version of the grammar automatically specialized to the more frequent constructions in the training corpus; and (3) allowing syntactic and semantic factors to interact with acoustic ones in the choice of a meaning structure for translation, so that the acoustically preferred hypothesis is not always selected even if it is within linguistic coverage.Comment: 9 pages, LaTeX. Published: Proceedings of TWLT-8, December 199