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

Combining Evidence from Unconstrained Spoken Term Frequency Estimation for Improved Speech Retrieval

This dissertation considers the problem of information retrieval in speech. Today's speech retrieval systems generally use a large vocabulary continuous speech recognition system to first hypothesize the words which were spoken. Because these systems have a predefined lexicon, words which fall outside of the lexicon can significantly reduce search quality---as measured by Mean Average Precision (MAP). This is particularly important because these Out-Of-Vocabulary (OOV) words are often rare and therefore good discriminators for topically relevant speech segments. The focus of this dissertation is on handling these out-of-vocabulary query words. The approach is to combine results from a word-based speech retrieval system with those from vocabulary-independent ranked utterance retrieval. The goal of ranked utterance retrieval is to rank speech utterances by the system's confidence that they contain a particular spoken word, which is accomplished by ranking the utterances by the estimated frequency of the word in the utterance. Several new approaches for estimating this frequency are considered, which are motivated by the disparity between reference and errorfully hypothesized phoneme sequences. The first method learns alternate pronunciations or degradations from actual recognition hypotheses and incorporates these variants into a new generative estimator for term frequency. A second method learns transformations of several easily computed features in a discriminative model for the same task. Both methods significantly improved ranked utterance retrieval in an experimental validation on new speech. The best of these ranked utterance retrieval methods is then combined with a word-based speech retrieval system. The combination approach uses a normalization learned in an additive model, which maps the retrieval status values from each system into estimated probabilities of relevance that are easily combined. Using this combination, much of the MAP lost because of OOV words is recovered. Evaluated on a collection of spontaneous, conversational speech, the system recovers 57.5\% of the MAP lost on short (title-only) queries and 41.3\% on longer (title plus description) queries

Incorporating Weak Statistics for Low-Resource Language Modeling

Automatic speech recognition (ASR) requires a strong language model to guide the acoustic model and favor likely utterances. While many tasks enjoy billions of language model training tokens, many domains which require ASR do not have readily available electronic corpora.The only source of useful language modeling data is expensive and time-consuming human transcription of in-domain audio. This dissertation seeks to quickly and inexpensively improve low-resource language modeling for use in automatic speech recognition. This dissertation first considers efficient use of non-professional human labor to best improve system performance, and demonstrate that it is better to collect more data, despite higher transcription error, than to redundantly transcribe data to improve quality. In the process of developing procedures to collect such data, this work also presents an efficient rating scheme to detect poor transcribers without gold standard data. As an alternative to this process, automatic transcripts are generated with an ASR system and explore efficiently combining these low-quality transcripts with a small amount of high quality transcripts. Standard n-gram language models are sensitive to the quality of the highest order n-gram and are unable to exploit accurate weaker statistics. Instead, a log-linear language model is introduced, which elegantly incorporates a variety of background models through MAP adaptation. This work introduces marginal class constraints which effectively capture knowledge of transcriber error and improve performance over n-gram features. Finally, this work constrains the language modeling task to keyword search of words unseen in the training text. While overall system performance is good, these words suffer the most due to a low probability in the language model. Semi-supervised learning effectively extracts likely n-grams containing these new keywords from a large corpus of audio. By using a search metric that favors recall over precision, this method captures over 80% of the potential gain

Linguistically-motivated sub-word modeling with applications to speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 173-185).Despite the proliferation of speech-enabled applications and devices, speech-driven human-machine interaction still faces several challenges. One of theses issues is the new word or the out-of-vocabulary (OOV) problem, which occurs when the underlying automatic speech recognizer (ASR) encounters a word it does not "know". With ASR being deployed in constantly evolving domains such as restaurant ratings, or music querying, as well as on handheld devices, the new word problem continues to arise.This thesis is concerned with the OOV problem, and in particular with the process of modeling and learning the lexical properties of an OOV word through a linguistically-motivated sub-syllabic model. The linguistic model is designed using a context-free grammar which describes the sub-syllabic structure of English words, and encapsulates phonotactic and phonological constraints. The context-free grammar is supported by a probability model, which captures the statistics of the parses generated by the grammar and encodes spatio-temporal context. The two main outcomes of the grammar design are: (1) sub-word units, which encode pronunciation information, and can be viewed as clusters of phonemes; and (2) a high-quality alignment between graphemic and sub-word units, which results in hybrid entities denoted as spellnemes. The spellneme units are used in the design of a statistical bi-directional letter-to-sound (L2S) model, which plays a significant role in automatically learning the spelling and pronunciation of a new word.The sub-word units and the L2S model are assessed on the task of automatic lexicon generation. In a first set of experiments, knowledge of the spelling of the lexicon is assumed. It is shown that the phonemic pronunciations associated with the lexicon can be successfully learned using the L2S model as well as a sub-word recognizer.(cont.) In a second set of experiments, the assumption of perfect spelling knowledge is relaxed, and an iterative and unsupervised algorithm, denoted as Turbo-style, makes use of spoken instances of both spellings and words to learn the lexical entries in a dictionary.Sub-word speech recognition is also embedded in a parallel fashion as a backoff mechanism for a word recognizer. The resulting hybrid model is evaluated in a lexical access application, whereby a word recognizer first attempts to recognize an isolated word. Upon failure of the word recognizer, the sub-word recognizer is manually triggered. Preliminary results show that such a hybrid set-up outperforms a large-vocabulary recognizer.Finally, the sub-word units are embedded in a flat hybrid OOV model for continuous ASR. The hybrid ASR is deployed as a front-end to a song retrieval application, which is queried via spoken lyrics. Vocabulary compression and open-ended query recognition are achieved by designing a hybrid ASR. The performance of the frontend recognition system is reported in terms of sentence, word, and sub-word error rates. The hybrid ASR is shown to outperform a word-only system over a range of out-of-vocabulary rates (1%-50%). The retrieval performance is thoroughly assessed as a fmnction of ASR N-best size, language model order, and the index size. Moreover, it is shown that the sub-words outperform alternative linguistically-motivated sub-lexical units such as phonemes. Finally, it is observed that a dramatic vocabulary compression - by more than a factor of 10 - is accompanied by a minor loss in song retrieval performance.by Ghinwa F. Choueiter.Ph.D