Minimising speaker verification utterance length through confidence based early verification decisions

This paper presents a novel approach of estimating the confidence interval of speaker verification scores. This approach is utilised to minimise the utterance lengths required in order to produce a confident verification decision. The confidence estimation method is also extended to address both the problem of high correlation in consecutive frame scores, and robustness with very limited training samples. The proposed technique achieves a drastic reduction in the typical data requirements for producing confident decisions in an automatic speaker verification system. When evaluated on the NIST 2005 SRE, the early verification decision method demonstrates that an average of 5–10 seconds of speech is sufficient to produce verification rates approaching those achieved previously using an average in excess of 100 seconds of speech.

Unsupervised speaker adaptation for telephone call transcription

The use of the PC and Internet for placing telephone calls will present new opportunities to capture vast amounts of un-transcribed speech for a particular speaker. This paper investigates how to best exploit this data for speaker-dependent speech recognition. Supervised and unsupervised experiments in acoustic model and language model adaptation are presented. Using one hour of automatically transcribed speech per speaker with a word error rate of 36.0%, unsupervised adaptation resulted in an absolute gain of 6.3%, equivalent to 70% of the gain from the supervised case, with additional adaptation data likely to yield further improvements. LM adaptation experiments suggested that although there seems to be a small degree of speaker idiolect, adaptation to the speaker alone, without considering the topic of the conversation, is in itself unlikely to improve transcription accuracy.

Developing a quick and practical screen to improve the identification of poor hydration in geriatric and rehabilitative care

Dehydration has been associated with increased morbidity and mortality. Dehydration risk increases with advancing age, and will progressively become an issue as the aging population increases. Worldwide, those aged 60 years and over are the fastest growing segment of the population. The study aimed to develop a clinically practical means to identify dehydration amongst older people in the clinical care setting. Older people aged 60 years or over admitted to the Geriatric and Rehabilitation Unit (GARU) of two tertiary teaching hospitals were eligible for participation in the study. Ninety potential screening questions and 38 clinical parameters were initially tested on a single sample (n=33) with the most promising 11 parameters selected to undergo further testing in an independent group (n=86). Of the almost 130 variables explored, tongue dryness was most strongly associated with poor hydration status, demonstrating 64% sensitivity and 62% specificity within the study participants. The result was not confounded by age, gender or body mass index. With minimal training, inter-rater repeatability was over 90%. This study identified tongue dryness as a potentially practical tool to identify dehydration risk amongst older people in the clinical care setting. Further studies to validate the potential screen in larger and varied populations of older people are required

Cascading appearance-based features for visual speaker verification

The cascading appearance-based (CAB) feature extraction technique has established itself as the state of the art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the speech recognition application also provide similar improvements for speaker recognition. These results suggest that visual speaker recognition can improve considerable when conducted solely through a consideration of the dynamic speech information rather than the static appearance of the speaker's mouth region.

Identification of factors predicting clickthrough in Web searching using neural network analysis

In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing

Bayes factor based speaker clustering for speaker diarization

This paper proposes the use of the Bayes Factor to replace the Bayesian Information Criterion (BIC) as a criterion for speaker clustering within a speaker diarization system. The BIC is one of the most popular decision criteria used in speaker diarization systems today. However, it will be shown in this paper that the BIC is only an approximation to the Bayes factor of marginal likelihoods of the data given each hypothesis. This paper uses the Bayes factor directly as a decision criterion for speaker clustering, thus removing the error introduced by the BIC approximation. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, leading to a 14.7% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Simulated cataracts and their effect on speech intelligibility

Purpose: The classic study of Sumby and Pollack (1954, JASA, 26(2), 212-215) demonstrated that visual information aided speech intelligibility under noisy auditory conditions. Their work showed that visual information is especially useful under low signal-to-noise conditions where the auditory signal leaves greater margins for improvement. We investigated whether simulated cataracts interfered with the ability of participants to use visual cues to help disambiguate the auditory signal in the presence of auditory noise. Methods: Participants in the study were screened to ensure normal visual acuity (mean of 20/20) and normal hearing (auditory threshold ≤ 20 dB HL). Speech intelligibility was tested under an auditory only condition and two visual conditions: normal vision and simulated cataracts. The light scattering effects of cataracts were imitated using cataract-simulating filters. Participants wore blacked-out glasses in the auditory only condition and lens-free frames in the normal auditory-visual condition. Individual sentences were spoken by a live speaker in the presence of prerecorded four-person background babble set to a speech-to-noise ratio (SNR) of -16 dB. The SNR was determined in a preliminary experiment to support 50% correct identification of sentence under the auditory only conditions. The speaker was trained to match the rate, intensity and inflections of a prerecorded audio track of everyday speech sentences. The speaker was blind to the visual conditions of the participant to control for bias.Participants’ speech intelligibility was measured by comparing the accuracy of their written account of what they believed the speaker to have said to the actual spoken sentence. Results: Relative to the normal vision condition, speech intelligibility was significantly poorer when participants wore simulated catarcts. Conclusions: The results suggest that cataracts may interfere with the acquisition of visual cues to speech perception.