nils-herrmann/clean_scirepeval huggingface dataset:
Feature selection for the classification of crosstalk in multi-channel audio

An extension to the conventional speech / nonspeech classification framework is presented for a scenario in which a number of microphones record the activity of speakers present at a meeting (one microphone per speaker). Since each microphone can receive speech from both the participant wearing the microphone (local speech) and other participants (crosstalk), the recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe a classifier in which a Gaussian mixture model (GMM) is used to model each class. A large set of potential acoustic features are considered, some of which have been employed in previous speech / nonspeech classifiers. A combination of two feature selection algorithms is used to identify the optimal feature set for each class. Results from the GMM classifier using the selected features are superior to those of a previously published approach.
Audio information access from meeting rooms

We investigate approaches to accessing information from the streams of audio data that result from multi-channel recordings of meetings. The methods investigated use word-level transcriptions, and information derived from models of speaker activity and speaker turn patterns. Our experiments include spoken document retrieval for meetings, automatic structuring of meetings based on self-similarity matrices of speaker turn patterns and a simple model of speaker activity. Meeting recordings are rich in both lexical and non-lexical information; our results illustrate some novel kinds of analysis made possible by a transcribed corpus of natural meetings.
Exploring the style-technique interaction in extractive summarization of broadcast news

In this paper we seek to explore the interaction between the style of a broadcast news story and its summarization technique. We report the performance of three different summarization techniques on broadcast news stories, which are split into planned speech and spontaneous speech. The initial results indicate that some summarization techniques work better for the documents with spontaneous speech than for those with planned speech. Even for human beings some documents are inherently difficult to summarize. We observe this correlation between degree of difficulty in summarizing and performance of the three automatic summarizers. Given the high frequency of named entities in broadcast news and even greater number of references to these named entities, we also gauge the effect of named entity and co-reference resolution in a news story, on the performance of these summarizers.
SVMSVM: support vector machine speaker verification methodology

Support vector machines with the Fisher and score-space kernels are used for text independent speaker verification to provide direct discrimination between complete utterances. This is unlike approaches such as discriminatively trained Gaussian mixture models or other discriminative classifiers that discriminate at the frame-level only. Using the sequence-level discrimination approach we are able to achieve error-rates that are significantly better than the current state-of-the-art on the PolyVar database.
Multi-class extractive voicemail summarization

This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and ‘other’. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words, with each word being identified by a vector of lexical and prosodic features. The features are selected using Parcel, an ROC-based algorithm. We visually compare the role of a large number of individual features and discuss effective ways to combine them. We finally evaluate their performance on manual and automatic transcriptions derived from two different speech recognition systems.
ASR system modeling for automatic evaluation and optimization of dialogue systems

Though the field of spoken dialogue systems has developed quickly in the last decade, rapid design of dialogue strategies remains uneasy. Several approaches to the problem of automatic strategy learning have been proposed and aie use of Reinforcement Learning introduced by Levin and Pieraccini is becoming part of the state of the art in this area. However, the quality of the strategy learned by the system depends on the definition of the optimization criterion and on the accuracy of aie environment model. In this paper, we propose to bring a model of an ASR system in the simulated environment in order to enhance the learned strategy. To do so, we introduced recognition error rates and confidence levels produced by ASR systems in the optimization criterion.
Annotation using Statistical Prosody Models

This paper is about the development of statistical models of prosodic features to generate linguistic meta-data for spoken language. In particular, we are concerned with automatically punctuating the output of a broadcast news speech recogniser. We present a statistical finite state model that combines prosodic, linguistic and punctuation class features. Experimental results are presented using the Hub–4 Broadcast News corpus, and in the light of our results we discuss the issue of a suitable method of evaluating the present task.
Punctuation annotation using statistical prosody models.

This paper is about the development of statistical models of prosodic features to generate linguistic meta-data for spoken language. In particular, we are concerned with automatically punctuating the output of a broadcast news speech recogniser. We present a statistical finite state model that combines prosodic, linguistic and punctuation class features. Experimental results are presented using the Hub–4 Broadcast News corpus, and in the light of our results we discuss the issue of a suitable method of evaluating the present task.
An advanced integrated architecture for wireless voicemail data retrieval

This paper describes an alternative architecture for voicemail data retrieval on the move. It is comprised of three distinct components: a speech recognizer, a text summarizer and a WAP push service initiator, enabling mobile users to receive text summaries of their voicemail in real-time without an explicit request. Our approach overcomes the cost and usability limitations of the conventional voicemail retrieval paradigm which requires a connection establishment in order to listen to spoken messages. We report performance results on all different components of the system that has been trained and tested on a database containing 1843 North American English messages as well as on the duration of the corresponding data path. The proposed architecture can be further customized to meet the requirements of a complex voicemail value-added service.
Extractive summarization of voicemail using lexical and prosodic feature subset selection

This paper presents a novel data-driven approach to summarizing spoken audio transcripts utilizing lexical and prosodic features. The former are obtained from a speech recognizer and the latter are extracted automatically from speech waveforms. We employ a feature subset selection algorithm, based on ROC curves, which examines different combinations of features at different target operating conditions. The approach is evaluated on the IBM Voicemail corpus, demonstrating that it is possible and desirable to avoid complete commitment to a single best classifier or feature set.
The Role of Prosody in a Voicemail Summarization System

When a speaker leaves a voicemail message there are prosodic cues that emphasize the important points in the message, in addition to lexical content. In this paper we compare and visualize the relative contribution of these two types of features within a voicemail summarization system. We describe the system’s ability to generate summaries of two test sets, having trained and validated using 700 messages from the IBM Voicemail corpus. Results measuring the quality of summary artifacts show that combined lexical and prosodic features are at least as robust as combined lexical features alone across all operating conditions.
Variable word rate N-grams

The rate of occurrence of words is not uniform but varies from document to document. Despite this observation, parameters for conventional N-gram language models are usually derived using the assumption of a constant word rate. In this paper we investigate the use of variable word rate assumption, modelled by a Poisson distribution or a continuous mixture of Poissons. We present an approach to estimating the relative frequencies of words or N-grams taking prior information of their occurrences into account. Discounting and smoothing schemes are also considered. Using the Broadcast News task, the approach demonstrates a reduction of perplexity up to 10%.
Statistical Language Modelling

Grammar-based natural language processing has reached a level where it can ‘understand’ language to a limited degree in restricted domains. For example, it is possible to parse textual material very accurately and assign semantic relations to parts of sentences. An alternative approach originates from the work of Shannon over half a century ago [41], . This approach assigns probabilities to linguistic events, where mathematical models are used to represent statistical knowledge. Once models are built, we decide which event is more likely than the others according to their probabilities. Although statistical methods currently use a very impoverished representation of speech and language (typically finite state), it is possible to train the underlying models from large amounts of data. Importantly, such statistical approaches often produce useful results. Statistical approaches seem especially well-suited to spoken language which is often spontaneous or conversational and not readily amenable to standard grammar-based approaches.
Transcription and summarization of voicemail speech

This paper describes the development of a system to transcribe and summarize voicemail messages. The results of the research presented in this paper are two-fold. First, a hybrid connectionist approach to the Voicemail transcription task shows that competitive performance can be achieved using a context-independent system with fewer parameters than those based on mixtures of Gaussian likelihoods. Second, an effective and robust combination of statistical with prior knowledge sources for term weighting is used to extract information from the decoder’s output in order to deliver summaries to the message recipients via a GSM Short Message Service (SMS) gateway.
Sentence Boundary Detection in Broadcast Speech Transcripts

This paper presents an approach to identifying sentence boundaries in broadcast speech transcripts. We describe finite state models that extract sentence boundary information statistically from text and audio sources. An n-gram language model is constructed from a collection of British English news broadcasts and scripts. An alternative model is estimated from pause duration information in speech recogniser outputs aligned with their programme script counterparts. Experimental results show that the pause duration model alone outperforms the language modelling approach and that, by combining these two models, it can be improved further and precision and recall scores of over 70% were attained for the task.
Information extraction from broadcast news

This paper discusses the development of trainable statistical models for extracting content from television and radio news broadcasts. In particular, we concentrate on statistical finite–state models for identifying proper names and other named entities in broadcast speech. Two models are presented: the first represents name class information as a word attribute; the second represents both word–word and class–class transitions explicitly. A common n–gram–based formulation is used for both models. The task of named–entity identification is characterized by relatively sparse training data, and issues related to smoothing are discussed. Experiments are reported using Hub–4E the DARPA/NIST evaluation for North American broadcast news.
Practical Identifiability of Finite Mixtures of Multivariate Bernoulli Distributions

The class of finite mixtures of multivariate Bernoulli distributions is known to be nonidentifiable; that is, different values of the mixture parameters can correspond to exactly the same probability distribution. In principle, this would mean that sample estimates using this model would give rise to different interpretations. We give empirical support to the fact that estimation of this class of mixtures can still produce meaningful results in practice, thus lessening the importance of the identifiability problem. We also show that the expectation-maximization algorithm is guaranteed to converge to a proper maximum likelihood estimate, owing to a property of the log-likelihood surface. Experiments with synthetic data sets show that an original generating distribution can be estimated from a sample. Experiments with an electropalatography data set show important structure in the data.
An Overview of the SPRACH System for the Transcription of Broadcast News

This paper describes the SPRACH system developed for the 1998 Hub-4E broadcast news evaluation. The system is based on the connectionist-HMM framework and uses both recurrent neural network and multi-layer perceptron acoustic models. We describe both a system designed for the primary transcription hub, and a system for the less-than 10 times real-time spoke. We then describe recent developments to CHRONOS, a time-first stack decoder. We show how these developments have simplified the evaluation system, and led to significant reductions in the error rate of the 10x real-time system. We also present a system designed to operate in real-time with negligible search error.
Recognition, indexing and retrieval of british broadcast news with the THISL system

This paper described the THISL spoken document retrieval system for British and North American Broadcast News. The system is based on the ABBOT large vocabulary speech recognizer and a probabilistic text retrieval system. We discuss the development of a realtime British English Broadcast News system, and its integration into a spoken document retrieval system. Detailed evaluation is performed using a similar North American Broadcast News system, to take advantage of the TREC SDR evaluation methodology. We report results on this evaluation, with particular reference to the effect of query expansion and of automatic segmentation algorithms.
The THISL SDR System At TREC-8

This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large vocabulary speech recognition system and a probabilistic text retrieval system. This paper describes the configuration of the speech recognition and text retrieval systems, including segmentation and query expansion. We report our results for development tests using the TREC-8 queries, and for the TREC-9 evaluation.
BASELINE IE-NE EXPERIMENTS USING THE SPRACH/LASIE SYSTEM

We have developed two conceptually different systems that are able to identify named entities from spoken audio. One (referred to as SPRACH-S) has a stochastic finite state machine structure for use with an acoustic model that identifies both words and named entities from speech data. The other (referred to as SPRACH-R) is a rulebased system which uses matching against stored name lists, partof-speech tagging, and light phrasal parsing with specialised named entity grammars. We provide an overview of the two approaches and present results on the Hub-4E IE-NE evaluation task.
Confidence measures from local posterior probability estimates

In this paper we introduce a set of related confidence measures for large vocabulary continuous speech recognition (LVCSR) based on local phone posterior probability estimates output by an acceptor HMM acoustic model. In addition to their computational efficiency, these confidence measures are attractive as they may be applied at the state-, phone-, word- or utterance-levels, potentially enabling discrimination between different causes of low confidence recognizer output, such as unclear acoustics or mismatched pronunciation models. We have evaluated these confidence measures for utterance verification using a number of different metrics. Experiments reveal several trends in “profitability of rejection", as measured by the unconditional error rate of a hypothesis test. These trends suggest that crude pronunciation models can mask the relatively subtle reductions in confidence caused by out-of-vocabulary (OOV) words and disfluencies, but not the gross model mismatches elicited by non-speech sounds. The observation that a purely acoustic confidence measure can provide improved performance over a measure based upon both acoustic and language model information for data drawn from the Broadcast News corpus, but not for data drawn from the North American Business News corpus suggests that the quality of model fit offered by a trigram language model is reduced for Broadcast News data. We also argue that acoustic confidence measures may be used to inform the search for improved pronunciation models.
Start-synchronous search for large vocabulary continuous speech recognition

In this paper, we present a novel, efficient search strategy for large vocabulary continuous speech recognition. The search algorithm, based on a stack decoder framework, utilizes phone-level posterior probability estimates (produced by a connectionist/hidden Markov model acoustic model) as a basis for phone deactivation pruning-a highly efficient method of reducing the required computation. The single-pass algorithm is naturally factored into the time-asynchronous processing of the word sequence and the time-synchronous processing of the hidden Markov model state sequence. This enables the search to be decoupled from the language model while still maintaining the computational benefits of time-synchronous processing. The incorporation of the language model in the search is discussed and computationally cheap approximations to the full language model are introduced. Experiments were performed on the North American Business News task using a 60000 word vocabulary and a trigram language model. Results indicate that the computational cost of the search may be reduced by more than a factor of 40 with a relative search error of less than 2% using the techniques discussed in the paper.
Statistical annotation of named entities in spoken audio.

In this paper we describe stochastic finite state model for named entity (NE) identification, based on explicit word-level -gram relations. NE categories are incorporated in the model as word attributes. We present an overview of the approach, describing how the extensible vocabulary model may be used for NE identification. We report development and evaluation results on a North American Broadcast News task. This approach resulted in average precision and recall scores of around 83% on hand transcribed data, and 73% on the SPRACH recogniser output. We also present an error analysis and a comparison of our approach with an alternative statistical approach.
Topic-based mixture language modelling

This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate the approach for mixture language modelling. A comparison is made between manual and automatic clustering in order to elucidate how the global content information is expressed in the space. We also compare (in terms of association with manual clustering and language modelling accuracy) alternative term-weighting schemes and the effect of singular value decomposition dimension reduction (latent semantic analysis). Test set perplexity results using the British National Corpus indicate that the approach can improve the potential of statistical language modelling. Using an adaptive procedure, the conventional model may be tuned to track text data with a slight increase in computational cost.
Named entity tagged language models

We introduce named entity (NE) language modelling, a stochastic finite state machine approach to identifying both words and NE categories from a stream of spoken data. We provide an overview of our approach to NE tagged language model (LM) generation together with results of the application of such a LM to the task of out-of-vocabulary (OOV) word reduction in large vocabulary speech recognition. Using the Wall Street Journal and Broadcast News corpora, it is shown that the tagged LM was able to reduce the overall word error rate by 14%, detecting up to 70% of previously OOV words. We also describe an example of the direct tagging of spoken data with NE categories.
RETRIEVAL SYSTEM DaveAbberley ( 1 ) , DavidKirby ( 2 ) , SteveRenals ( 1 ) andTonyRobinson ( 3 ) ( 1 )

This paper described the THISL spoken document retrieval system for British and North American Broadcast News. The system is based on the ABBOT large vocabulary speech recognizer, using a recurrent network acoustic model, and a probabilistic text retrieval system. We discuss the development of a realtime British En-glish Broadcast News system, and its integration into a spoken document retrieval system. Detailed evaluation is performed using a similar North American Broadcast News system, to take advantage of the TREC SDR evaluation methodology. We report results on this evaluation , with particular reference to the effect of query expansion and of automatic segmentation algorithms.
The THISL broadcast news retrieval system.

This paper described the THISL spoken document retrieval system for British and North American Broadcast News. The system is based on the ABBOT large vocabulary speech recognizer, using a recurrent network acoustic model, and a probabilistic text retrieval system. We discuss the development of a realtime British English Broadcast News system, and its integration into a spoken document retrieval system. Detailed evaluation is performed using a similar North American Broadcast News system, to take advantage of the TREC SDR evaluation methodology. We report results on this evaluation, with particular reference to the effect of query expansion and of automatic segmentation algorithms.
The THISL system for indexing and retrieval of broadcast news

This paper describes the THISL news retrieval system which maintains an archive of BBC radio and television news recordings. The system uses the ABBOT large vocabulary continuous speech recognition system to transcribe news broadcasts, and the thisIIR text retrieval system to index and access the transcripts. Decoding and indexing is performed automatically, and the archive is updated with three hours of new material every day. A Web-based interface to the retrieval system has been devised to facilitate access to the archive.