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

Quantifying the value of pronunciation lexicons for keyword search in low resource languages

ABSTRACT This paper quantifies the value of pronunciation lexicons in large vocabulary continuous speech recognition (LVCSR) systems that support keyword search (KWS) in low resource languages. Stateof-the-art LVCSR and KWS systems are developed for conversational telephone speech in Tagalog, and the baseline lexicon is augmented via three different grapheme-to-phoneme models that yield increasing coverage of a large Tagalog word-list. It is demonstrated that while the increased lexical coverage -or reduced out-of-vocabulary (OOV) rate -leads to only modest (ca 1%-4%) improvements in word error rate, the concomitant improvements in actual term weighted value are as much as 60%. It is also shown that incorporating the augmented lexicons into the LVCSR system before indexing speech is superior to using them post facto, e.g., for approximate phonetic matching of OOV keywords in pre-indexed lattices. These results underscore the disproportionate importance of automatic lexicon augmentation for KWS in morphologically rich languages, and advocate for using them early in the LVCSR stage. Index Terms-Speech Recognition, Keyword Search, Information Retrieval, Morphology, Speech Synthesis LOW-RESOURCE KEYWORD SEARCH Thanks in part to the falling costs of storage and transmission, large volumes of speech such as oral history archives [1, 2] and on-line lectures We are interested in improving KWS performance in a low resource setting, i.e. where some resources are available to develop The authors, listed here in alphabetical order, were supported by DARPA BOLT contract Nō HR0011-12-C-0015, and IARPA BABEL contract Nō W911NF-12-C-0015. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, IARPA, DoD/ARL or the U.S. Government. an LVCSR system -such as 10 hours of transcribed speech corresponding to about 100K words of transcribed text, and a pronunciation lexicon that covers the words in the training data -but accuracy is sufficiently low that considerable improvement in KWS performance is necessary before the system is usable for searching a speech collection. A fair amount of past research has been devoted to improving the acoustic models from un-transcribed speech The importance of pronunciation lexicons for LVCSR is not entirely underestimated. Several papers have addressed the problem of automatically generating pronunciations for out of vocabulary (OOV) words Two notable exceptions to this conventional wisdom are (i) accuracy on infrequent, content-bearing words, which are more likely to be OOV, and (ii) accuracy in morphologically rich languages, e.g. Czech and Turkish. These exceptions come together in a detrimental fashion when developing KWS systems for a morphologically rich, low resource language such as Tagalog. This is the setting in which we will quantify the impact of increasing lexical coverage on the performance of a KWS system. We assume a transcribed corpus of 10 hours of Tagalog conversational telephone speech We first develop state-of-the-art LVCSR and KWS systems based on the given resources. We process and index a 10 hour search collection using the KWS system, and measure KWS performance using a set of 355 Tagalog queries. We then explore three different methods for augmenting the 5.7K word lexicon to include additional words seen in the larger LM training corpus. The augmented lexicons are used to improve the KWS system in two different ways: reprocessing the speech with the larger lexicon, or using it during keyword search. The efficacy of the augmented lexicons is measured in terms of 8560 978-1-4799-0356-6/13/$31.0

Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR

Language modeling for speech recognition of spoken Cantonese.

Yeung, Yu Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 84-93).Abstracts in English and Chinese.Acknowledgement --- p.iiiAbstract --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Cantonese Speech Recognition --- p.3Chapter 1.2 --- Objectives --- p.4Chapter 1.3 --- Thesis Outline --- p.5Chapter 2 --- Fundamentals of Large Vocabulary Continuous Speech Recognition --- p.7Chapter 2.1 --- Problem Formulation --- p.7Chapter 2.2 --- Feature Extraction --- p.8Chapter 2.3 --- Acoustic Models --- p.9Chapter 2.4 --- Decoding --- p.10Chapter 2.5 --- Statistical Language Modeling --- p.12Chapter 2.5.1 --- N-gram Language Models --- p.12Chapter 2.5.2 --- N-gram Smoothing --- p.13Chapter 2.5.3 --- Complexity of Language Model --- p.15Chapter 2.5.4 --- Class-based Langauge Model --- p.16Chapter 2.5.5 --- Language Model Pruning --- p.17Chapter 2.6 --- Performance Evaluation --- p.18Chapter 3 --- The Cantonese Dialect --- p.19Chapter 3.1 --- Phonology of Cantonese --- p.19Chapter 3.2 --- Orthographic Representation of Cantonese --- p.22Chapter 3.3 --- Classification of Cantonese speech --- p.25Chapter 3.4 --- Cantonese-English Code-mixing --- p.27Chapter 4 --- Rule-based Translation Method --- p.29Chapter 4.1 --- Motivations --- p.29Chapter 4.2 --- Transformation-based Learning --- p.30Chapter 4.2.1 --- Algorithm Overview --- p.30Chapter 4.2.2 --- Learning of Translation Rules --- p.32Chapter 4.3 --- Performance Evaluation --- p.35Chapter 4.3.1 --- The Learnt Translation Rules --- p.35Chapter 4.3.2 --- Evaluation of the Rules --- p.37Chapter 4.3.3 --- Analysis of the Rules --- p.37Chapter 4.4 --- Preparation of Training Data for Language Modeling --- p.41Chapter 4.5 --- Discussion --- p.43Chapter 5 --- Language Modeling for Cantonese --- p.44Chapter 5.1 --- Training Data --- p.44Chapter 5.1.1 --- Text Corpora --- p.44Chapter 5.1.2 --- Preparation of Formal Cantonese Text Data --- p.45Chapter 5.2 --- Training of Language Models --- p.46Chapter 5.2.1 --- Language Models for Standard Chinese --- p.46Chapter 5.2.2 --- Language Models for Formal Cantonese --- p.46Chapter 5.2.3 --- Language models for Colloquial Cantonese --- p.47Chapter 5.3 --- Evaluation of Language Models --- p.48Chapter 5.3.1 --- Speech Corpora for Evaluation --- p.48Chapter 5.3.2 --- Perplexities of Formal Cantonese Language Models --- p.49Chapter 5.3.3 --- Perplexities of Colloquial Cantonese Language Models --- p.51Chapter 5.4 --- Speech Recognition Experiments --- p.53Chapter 5.4.1 --- Speech Corpora --- p.53Chapter 5.4.2 --- Experimental Setup --- p.54Chapter 5.4.3 --- Results on Formal Cantonese Models --- p.55Chapter 5.4.4 --- Results on Colloquial Cantonese Models --- p.56Chapter 5.5 --- Analysis of Results --- p.58Chapter 5.6 --- Discussion --- p.59Chapter 5.6.1 --- Cantonese Language Modeling --- p.59Chapter 5.6.2 --- Interpolated Language Models --- p.59Chapter 5.6.3 --- Class-based Language Models --- p.60Chapter 6 --- Towards Language Modeling of Code-mixing Speech --- p.61Chapter 6.1 --- Data Collection --- p.61Chapter 6.1.1 --- Data Collection --- p.62Chapter 6.1.2 --- Filtering of Collected Data --- p.63Chapter 6.1.3 --- Processing of Collected Data --- p.63Chapter 6.2 --- Clustering of Chinese and English Words --- p.64Chapter 6.3 --- Language Modeling for Code-mixing Speech --- p.64Chapter 6.3.1 --- Language Models from Collected Data --- p.64Chapter 6.3.2 --- Class-based Language Models --- p.66Chapter 6.3.3 --- Performance Evaluation of Code-mixing Language Models --- p.67Chapter 6.4 --- Speech Recognition Experiments with Code-mixing Language Models --- p.69Chapter 6.4.1 --- Experimental Setup --- p.69Chapter 6.4.2 --- Monolingual Cantonese Recognition --- p.70Chapter 6.4.3 --- Code-mixing Speech Recognition --- p.72Chapter 6.5 --- Discussion --- p.74Chapter 6.5.1 --- Data Collection from the Internet --- p.74Chapter 6.5.2 --- Speech Recognition of Code-mixing Speech --- p.75Chapter 7 --- Conclusions and Future Work --- p.77Chapter 7.1 --- Conclusions --- p.77Chapter 7.1.1 --- Rule-based Translation Method --- p.77Chapter 7.1.2 --- Cantonese Language Modeling --- p.78Chapter 7.1.3 --- Code-mixing Language Modeling --- p.78Chapter 7.2 --- Future Works --- p.79Chapter 7.2.1 --- Rule-based Translation --- p.79Chapter 7.2.2 --- Training data --- p.80Chapter 7.2.3 --- Code-mixing speech --- p.80Chapter A --- Equation Derivation --- p.82Chapter A.l --- Relationship between Average Mutual Information and Perplexity --- p.82Bibliography --- p.8