Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation

This paper presents a new method for the discovery of latent domains in diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech Recognition. Our work focuses on transcription of multi-genre broadcast media, which is often only categorised broadly in terms of high level genres such as sports, news, documentary, etc. However, in terms of acoustic modelling these categories are coarse. Instead, it is expected that a mixture of latent domains can better represent the complex and diverse behaviours within a TV show, and therefore lead to better and more robust performance. We propose a new method, whereby these latent domains are discovered with Latent Dirichlet Allocation, in an unsupervised manner. These are used to adapt DNNs using the Unique Binary Code (UBIC) representation for the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more than 2,000 shows for training and 47 shows for testing, show that the use of LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline hybrid DNN models

Combining feature and model-based adaptation of RNNLMs for multi-genre broadcast speech recognition

Recurrent neural network language models (RNNLMs) have consistently outperformed n-gram language models when used in automatic speech recognition (ASR). This is because RNNLMs provide robust parameter estimation through the use of a continuous-space representation of words, and can generally model longer context dependencies than n-grams. The adaptation of RNNLMs to new domains remains an active research area and the two main approaches are: feature-based adaptation, where the input to the RNNLM is augmented with auxiliary features; and model-based adaptation, which includes model fine-tuning and introduction of adaptation layer(s) in the network. This paper explores the properties of both types of adaptation on multi-genre broadcast speech recognition. Two hybrid adaptation techniques are proposed, namely the finetuning of feature-based RNNLMs and the use of a feature-based adaptation layer. A method for the semi-supervised adaptation of RNNLMs, using topic model-based genre classification, is also presented and investigated. The gains obtained with RNNLM adaptation on a system trained on 700h. of speech are consistent using both RNNLMs trained on a small (10Mwords) and large set (660M words), with 10% perplexity and 2% word error rate improvements on a 28:3h. test set

Background-tracking acoustic features for genre identification of broadcast shows

This paper presents a novel method for extracting acoustic features that characterise the background environment in audio recordings. These features are based on the output of an alignment that fits multiple parallel background-based Constrained Maximum Likelihood Linear Regression transformations asynchronously to the input audio signal. With this setup, the resulting features can track changes in the audio background like appearance and disappearance of music, applause or laughter, independently of the speakers in the foreground of the audio. The ability to provide this type of acoustic description in audiovisual data has many potential applications, including automatic classification of broadcast archives or improving automatic transcription and subtitling. In this paper, the performance of these features in a genre identification task in a set of 332 BBC shows is explored. The proposed background-tracking features outperform short-term Perceptual Linear Prediction features in this task using Gaussian Mixture Model classifiers (62% vs 72% accuracy). The use of more complex classifiers, Hidden Markov Models and Support Vector Machines, increases the performance of the system with the novel background-tracking features to 79% and 81% in accuracy respectively

Data-Selective Transfer Learning for Multi-Domain Speech Recognition

Negative transfer in training of acoustic models for automatic speech recognition has been reported in several contexts such as domain change or speaker characteristics. This paper proposes a novel technique to overcome negative transfer by efficient selection of speech data for acoustic model training. Here data is chosen on relevance for a specific target. A submodular function based on likelihood ratios is used to determine how acoustically similar each training utterance is to a target test set. The approach is evaluated on a wide–domain data set, covering speech from radio and TV broadcasts, telephone conversations, meetings, lectures and read speech. Experiments demonstrate that the proposed technique both finds relevant data and limits negative transfer. Results on a 6–hour test set show a relative improvement of 4% with data selection over using all data in PLP based models, and 2% with DNN feature

The 2015 Sheffield System for Longitudinal Diarisation of Broadcast Media

Speaker diarisation is the task of answering "who spoke when" within a multi-speaker audio recording. Diarisation of broadcast media typically operates on individual television shows, and is a particularly difficult task, due to a high number of speakers and challenging background conditions. Using prior knowledge, such as that from previous shows in a series, can improve performance. Longitudinal diarisation allows to use knowledge from previous audio files to improve performance, but requires finding matching speakers across consecutive files. This paper describes the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge. The challenge required longitudinal diarisation of data from BBC archives, under very constrained resource settings. Our system consists of three main stages: speech activity detection using DNNs with novel adaptation and decoding methods; speaker segmentation and clustering, with adaptation of the DNN-based clustering models; and finally speaker linking to match speakers across shows. The final result on the development set of 19 shows from five different television series provided a Diarisation Error Rate of 50.77% in the diarisation and linking task

Unsupervised Domain Discovery Using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition

Speech recognition systems are often highly domain dependent, a fact widely reported in the literature. However the concept of domain is complex and not bound to clear criteria. Hence it is often not evident if data should be considered to be out-of-domain. While both acoustic and language models can be domain specific, work in this paper concentrates on acoustic modelling. We present a novel method to perform unsupervised discovery of domains using Latent Dirichlet Allocation (LDA) modelling. Here a set of hidden domains is assumed to exist in the data, whereby each audio segment can be considered to be a weighted mixture of domain properties. The classification of audio segments into domains allows the creation of domain specific acoustic models for automatic speech recognition. Experiments are conducted on a dataset of diverse speech data covering speech from radio and TV broadcasts, telephone conversations, meetings, lectures and read speech, with a joint training set of 60 hours and a test set of 6 hours. Maximum A Posteriori (MAP) adaptation to LDA based domains was shown to yield relative Word Error Rate (WER) improvements of up to 16% relative, compared to pooled training, and up to 10%, compared with models adapted with human-labelled prior domain knowledge

The 2015 Sheffield System for Transcription of Multi–Genre Broadcast Media

We describe the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge task of transcribing multi-genre broadcast shows. Transcription was one of four tasks proposed in the MGB challenge, with the aim of advancing the state of the art of automatic speech recognition, speaker diarisation and automatic alignment of subtitles for broadcast media. Four topics are investigated in this work: Data selection techniques for training with unreliable data, automatic speech segmentation of broadcast media shows, acoustic modelling and adaptation in highly variable environments, and language modelling of multi-genre shows. The final system operates in multiple passes, using an initial unadapted decoding stage to refine segmentation, followed by three adapted passes: a hybrid DNN pass with input features normalised by speaker-based cepstral normalisation, another hybrid stage with input features normalised by speaker feature-MLLR transformations, and finally a bottleneck-based tandem stage with noise and speaker factorisation. The combination of these three system outputs provides a final error rate of 27.5% on the official development set, consisting of 47 multi-genre shows

Recurrent neural network language model adaptation for multi-genre broadcast speech recognition and alignment

Recurrent neural network language models (RNNLMs) generally outperform n-gram language models when used in automatic speech recognition. Adapting RNNLMs to new domains is an open problem and current approaches can be categorised as either feature-based and model-based. In feature-based adaptation, the input to the RNNLM is augmented with auxiliary features whilst model-based adaptation includes model fine-tuning and the introduction of adaptation layer(s) in the network. In this paper, the properties of both types of adaptation are investigated on multi-genre broadcast speech recognition. Existing techniques for both types of adaptation are reviewed and the proposed techniques for model-based adaptation, namely the linear hidden network (LHN) adaptation layer and the K-component adaptive RNNLM, are investigated. Moreover, new features derived from the acoustic domain are investigated for RNNLM adaptation. The contributions of this paper include two hybrid adaptation techniques: the fine-tuning of feature-based RNNLMs and a feature-based adaptation layer. Moreover, the semi-supervised adaptation of RNNLMs using genre information is also proposed. The ASR systems were trained using 700h of multi-genre broadcast speech. The gains obtained when using the RNNLM adaptation techniques proposed in this work are consistent when using RNNLMs trained on an in-domain set of 10M words and on a combination of in-domain and out-of-domain sets of 660M words, with approx. 10% perplexity and 2% relative word error rate improvements on a 28.3h. test set. The best RNNLM adaptation techniques for ASR are also evaluated on a lightly supervised alignment of subtitles task for the same data, where the use of RNNLM adaptation leads to an absolute increase in the F-measure of 0.5%

The USFD Spoken Language Translation System for IWSLT 2014

The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data