Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

The Atlas of North American English: Phonetics, Phonology and Sound Change. A Multimedia Reference Tool

PHONOLOGY AND LANGUAGE USE. Joan Bybee. New York: Cambridge University Press, 2001. Pp. xviii + 237. $80.00 cloth, $32.00 paper. Bybee is concerned with providing an understanding of a vast amount of evidence from language use that remains unaccounted for in the usual models of generative phonology, including its currently popular descendant, Optimality Theory.

Phonology and language use

Language Death: Preface

In this work, we unveil new privacy threats against Voice-over-IP (VoIP) communications. Although prior work has shown that the interaction of variable bit-rate codecs and length-preserving stream ciphers leaks information, we show that the threat is more serious than previously thought. In particular, we derive approximate transcripts of encrypted VoIP conversations by segmenting an observed packet stream into subsequences representing individual phonemes and classifying those subsequences by the phonemes they encode. Drawing on insights from the computational linguistics and speech recognition communities, we apply novel techniques for unmasking parts of the conversation. We believe our ability to do so underscores the importance of designing secure (yet efficient) ways to protect the confidentiality of VoIP conversations.

/pdf/phonotactic-reconstruction-of-encrypted-voip-conversations-36wudyk0dl.pdf

Phonotactic Reconstruction of Encrypted VoIP Conversations: Hookt on Fon-iks

Discovering the phoneme inventory of an unwritten language: A machine-assisted approach

Cross-language speech recognition often assumes a certain amount of knowledge about the target language. However, there are hundreds of languages where not even the phoneme inventory is known. In the work reported here, phone recognisers are evaluated on a cross-language task with minimum target knowledge. A phonetic distance measure is introduced for the evaluation, allowing a distance to be calculated between any utterance of any language. This has a number of spin-off applications such as allophone detection, a phone-based ROVER approach to recognition, and cross-language forced alignment. Results show that some of these novel approaches will be of immediate use in characterising languages where there is little phonological knowledge.

Cross-Language Phone Recognition when the Target Language Phoneme Inventory is not Known.

In this paper we present a corpus of audio and video recordings of spontaneous, face-to-face multi-party conversation in two languages. Freely available high quality recordings of mundane, non-institutional, multi-party talk are still sparse, and this corpus aims to contribute valuable data suitable for study of multiple aspects of spoken interaction. In particular, it constitutes a unique resource for spoken Bosnian Serbo-Croatian (BSC), an under-resourced language with no spoken resources available at present. The corpus consists of just over 3 hours of free conversation in each of the target languages, BSC and British English (BE). The audio recordings have been made on separate channels using head-set microphones, as well as using a microphone array, containing 8 omni-directional microphones. The data has been segmented and transcribed using segmentation notions and transcription conventions developed from those of the conversation analysis research tradition. Furthermore, the transcriptions have been automatically aligned with the audio at the word and phone level, using the method of forced alignment. In this paper we describe the procedures behind the corpus creation and present the main features of the corpus for the study of conversation.

/pdf/a-corpus-of-spontaneous-multi-party-conversation-in-bosnian-336dphwl0i.pdf

A Corpus of Spontaneous Multi-party Conversation in Bosnian Serbo-Croatian and British English

There is a consensus between many linguists that half of all languages risk disappearing by the end of the century. Documentation is agreed to be a priority. This includes the process of phonemic analysis to discover the contrastive sounds of a language with the resulting benefits of further linguistic analysis, literacy, and access to speech technology. A machine-assisted approach to phonemic analysis has the potential to greatly speed up the process and make the analysis more objective.
 
Good computer tools are already available to help in a phonemic analysis, but these primarily provide search and sort database functionality, rather than automated analysis. In computational phonology there have been very few studies on the automated discovery of phonological patterns from surface level data such as narrow phonetic transcriptions or acoustics.
 
This thesis addresses the lack of research in this area. The key scientific question underpinning the work in this thesis is "To what extent can a machine algorithm contribute to the procedures needed for a phonemic analysis?". A secondary question is "What insights does such a quantitative evaluation give about the contribution of each of these procedures to a phonemic analysis?"
 
It is demonstrated that a machine-assisted approach can make a measurable contribution to a phonemic analysis for all the procedures investigated; phonetic similarity, phone recognition & alignment, complementary distribution, and minimal pairs. The evaluation measures introduced in this thesis allows a comprehensive quantitative comparison between these phonemic analysis procedures. Given the best available data and the machine-assisted procedures described, there is a strong indication that phonetic similarity is the most important piece of evidence in a phonemic analysis.

The tools and techniques developed in this thesis have resulted in tangible benefits to the analysis of two under-resourced languages and it is expected that many more languages will follow.

/pdf/machine-assisted-phonemic-analysis-x0t3oytq7z.pdf

Machine-assisted phonemic analysis

Language Identification (LID) of speech can be split into two processes; phone recognition and language modelling. This two stage approach underlies some of the most successful LID systems. As phone recognizers become more accurate it is useful to simulate a very accurate phone recognizer to determine the effect on the overall LID accuracy. This can be done by using phone transcripts. In this paper LID is performed on phone transcripts from six different languages in the OGI multi-language telephone speech corpus. By simulating a phone recognizer that classifies phones into ten broad classes, a simple n-gram model gives low LID equal error rates (EER) of <1% on 30 seconds of test data. Language models based on these accurate phone transcripts can reveal insights into the phonology of different languages.

Timothy Kempton

Papers

Discovering the phoneme inventory of an unwritten language: A machine-assisted approach

Cross-Language Phone Recognition when the Target Language Phoneme Inventory is not Known.

A Corpus of Spontaneous Multi-party Conversation in Bosnian Serbo-Croatian and British English

Machine-assisted phonemic analysis

Language identification: insights from the classification of hand annotated phone transcripts.