A comparison of phone and grapheme-based spoken term detection

We propose grapheme-based sub-word units for spoken term detection (STD). Compared to phones, graphemes have a number of potential advantages. For out-of-vocabulary search terms, phone- based approaches must generate a pronunciation using letter-to-sound rules. Using graphemes obviates this potentially error-prone hard decision, shifting pronunciation modelling into the statistical models describing the observation space. In addition, long-span grapheme language models can be trained directly from large text corpora. We present experiments on Spanish and English data, comparing phone and grapheme-based STD. For Spanish, where phone and grapheme-based systems give similar transcription word error rates (WERs), grapheme-based STD significantly outperforms a phone- based approach. The converse is found for English, where the phone-based system outperforms a grapheme approach. However, we present additional analysis which suggests that phone-based STD performance levels may be achieved by a grapheme-based approach despite lower transcription accuracy, and that the two approaches may usefully be combined. We propose a number of directions for future development of these ideas, and suggest that if grapheme-based STD can match phone-based performance, the inherent flexibility in dealing with out-of-vocabulary terms makes this a desirable approach

Subword-based Indexing for a Minimal False Positive Rate

Subword-based Indexing for a Minimal False Positive Rat

Strategies for Representing Tone in African Writing Systems

Tone languages provide some interesting challenges for the designers of new orthographies. One approach is to omit tone marks, just as stress is not marked in English (zero marking). Another approach is to do phonemic tone analysis and then make heavy use of diacritic symbols to distinguish the `tonemes' (exhaustive marking). While orthographies based on either system have been successful, this may be thanks to our ability to manage inadequate orthographies rather than to any intrinsic advantage which is afforded by one or the other approach. In many cases, practical experience with both kinds of orthography in sub-Saharan Africa has shown that people have not been able to attain the level of reading and writing fluency that we know to be possible for the orthographies of non-tonal languages. In some cases this can be attributed to a sociolinguistic setting which does not favour vernacular literacy. In other cases, the orthography itself might be to blame. If the orthography of a tone language is difficult to user or to learn, then a good part of the reason, I believe, is that the designer either has not paid enough attention to the function of tone in the language, or has not ensured that the information encoded in the orthography is accessible to the ordinary (non-linguist) user of the language. If the writing of tone is not going to continue to be a stumbling block to literacy efforts, then a fresh approach to tone orthography is required, one which assigns high priority to these two factors. This article describes the problems with orthographies that use too few or too many tone marks, and critically evaluates a wide range of creative intermediate solutions. I review the contributions made by phonology and reading theory, and provide some broad methodological principles to guide someone who is seeking to represent tone in a writing system. The tone orthographies of several languages from sub-Saharan Africa are presented throughout the article, with particular emphasis on some tone languages of Cameroon

A comparison of grapheme and phoneme-based units for Spanish spoken term detection

The ever-increasing volume of audio data available online through the world wide web means that automatic methods for indexing and search are becoming essential. Hidden Markov model (HMM) keyword spotting and lattice search techniques are the two most common approaches used by such systems. In keyword spotting, models or templates are defined for each search term prior to accessing the speech and used to find matches. Lattice search (referred to as spoken term detection), uses a pre-indexing of speech data in terms of word or sub-word units, which can then quickly be searched for arbitrary terms without referring to the original audio. In both cases, the search term can be modelled in terms of sub-word units, typically phonemes. For in-vocabulary words (i.e. words that appear in the pronunciation dictionary), the letter-to-sound conversion systems are accepted to work well. However, for out-of-vocabulary (OOV) search terms, letter-to-sound conversion must be used to generate a pronunciation for the search term. This is usually a hard decision (i.e. not probabilistic and with no possibility of backtracking), and errors introduced at this step are difficult to recover from. We therefore propose the direct use of graphemes (i.e., letter-based sub-word units) for acoustic modelling. This is expected to work particularly well in languages such as Spanish, where despite the letter-to-sound mapping being very regular, the correspondence is not one-to-one, and there will be benefits from avoiding hard decisions at early stages of processing. In this article, we compare three approaches for Spanish keyword spotting or spoken term detection, and within each of these we compare acoustic modelling based on phone and grapheme units. Experiments were performed using the Spanish geographical-domain Albayzin corpus. Results achieved in the two approaches proposed for spoken term detection show us that trigrapheme units for acoustic modelling match or exceed the performance of phone-based acoustic models. In the method proposed for keyword spotting, the results achieved with each acoustic model are very similar

Using Resources from a Closely-related Language to Develop ASR for a Very Under-resourced Language: A Case Study for Iban

International audienceThis paper presents our strategies for developing an automatic speech recognition system for Iban, an under-resourced language. We faced several challenges such as no pronunciation dictionary and lack of training material for building acoustic models. To overcome these problems, we proposed approaches which exploit resources from a closely-related language (Malay). We developed a semi-supervised method for building the pronunciation dictionary and applied cross-lingual strategies for improving acoustic models trained with very limited training data. Both approaches displayed very encouraging results, which show that data from a closely-related language, if available, can be exploited to build ASR for a new language. In the final part of the paper, we present a zero-shot ASR using Malay resources that can be used as an alternative method for transcribing Iban speech

A Unified Model of Thai Romanization and Word Segmentation

Thai romanization is the way to write Thai language using roman alphabets. It could be performed on the basis of orthographic form (transliteration) or pronunciation (transcription) or both. As a result, many systems of romanization are in use. The Royal Institute has established the standard by proposing the principle of romanization on the basis of transcription. To ensure the standard, a fully automatic Thai romanization system should be publicly made available. In this paper, we discuss the problems of Thai Romanization. We argue that automatic Thai romanization is difficult because the ambiguities of pronunciation are caused not only by the ambiguities of syllable segmentation, but also by the ambiguities of word segmentation. A model of automatic romanization then is designed and implemented on this ground. The problem of romanization and word segmentation are handled simultaneously. A syllable-segmented corpus and a corpus of word-pronunciation are used for training the system. The accuracy of the system is 94.44% for unseen names and 99.58% for general texts. When the training corpus includes some proper names, the accuracy of romanizing unseen names was increased from 94.44% to 97%. Our system performs well because it is designed to better suit the problem