This paper proposes the adaptation of neural network-based acoustic models using a Squeeze-and-Excitation (SE) network for automatic speech recognition (ASR). In particular, this work explores to use the SE network to learn utterance-level embeddings. The acoustic modelling is performed using Light Gated Recurrent Units (LiGRU). The utterance embed-dings are learned from hidden unit activations jointly with LiGRU and used to scale respective activations of hidden layers in the LiGRU network. The advantage of such approach is that it does not require domain labels, such as speakers and noise to be known in order to perform the adaptation, thereby providing unsupervised adaptation. The global average and attentive pooling are applied on hidden units to extract utterance-level information that represents the speakers and acoustic conditions. ASR experiments were carried out on the TIMIT and Aurora 4 corpora. The proposed model achieves better performance on both the datasets compared to their respective baselines with relative improvements of 5.59% and 5.54% for TIMIT and Aurora 4 database, respectively. These experiments show the potential of using the conditioning information learned via utterance embeddings in the SE network to adapt acoustic models for speakers, noise, and other acoustic conditions.

Recurrent neural network language models (RNNLMs) generally outperform n-gram language models when used in automatic speech recognition (ASR). Adapting RNNLMs to new domains is an open problem and current approaches can be categorised as either feature-based or 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 adaptation layer and the K-component adaptive the RNNLM, are investigated. Moreover, new features derived from the acoustic domain are investigated for the 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 700 h of multi-genre broadcast speech. The gains obtained when using the RNNLM adaptation techniques proposed in this paper 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 660 M words, with approx. 10% perplexity and 2% relative word error rate improvements on a 28.3 h. 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%.

Applications of automatic speech recognition (ASR) such as broadcast transcription and dialog systems, can be helped by the ability to detect errors in the ASR output. The field of ASR error detection has emerged as a way to detect and subsequently correct ASR errors. The most common approach for ASR error detection is features-based, where a set of features are extracted from the ASR output and used to train a classifier to predict correct/incorrect labels.Language models (LMs), either from the ASR decoder or externally trained, can be used to provide features to an ASR error detection system, through scores computed on the ASR output. Recently, recurrent neural network language models (RNNLMs) features were proposed for ASR error detection with improvements to the classification rate, thanks to their ability to model longer-range context.RNNLM adaptation, through the introduction of auxiliary features that encode domain, has been shown to improve ASR performance. This work investigates whether RNNLM adaptation techniques can also improve ASR error detection performance in the context of multi-genre broadcast ASR. The results show that an overall improvement of about 1% in the F-measure can be achieved using adapted RNNLM features.

Abstract This paper describes a system for performing alignment of subtitles to audio on multigenre broadcasts using a lightly supervised approach. Accurate alignment of subtitles plays a substantial role in the daily work of media companies and currently still requires large human effort. Here, a comprehensive approach to performing this task in an automated way using lightly supervised alignment is proposed. The paper explores the different alternatives to speech segmentation, lightly supervised speech recognition and alignment of text streams. The proposed system uses lightly supervised decoding to improve the alignment accuracy by performing language model adaptation using the target subtitles. The system thus built achieves the third best reported result in the alignment of broadcast subtitles in the Multi–Genre Broadcast (MGB) challenge, with an F1 score of 88.8%. This system is available for research and other non–commercial purposes through webASR, the University of Sheffield’s cloud–based speech technology web service. Taking as inputs an audio file and untimed subtitles, webASR can produce timed subtitles in multiple formats, including TTML, WebVTT and SRT.

Neural Machine Translation (NMT) has recently demonstrated improved performance over statistical machine translation and relies on an encoder-decoder framework for translating text from source to target. The structure of NMT makes it amenable to add auxiliary features, which can provide complementary information to that present in the source text. In this paper, auxiliary features derived from accompanying audio, are investigated for NMT and are compared and combined with text-derived features. These acoustic embeddings can help resolve ambiguity in the translation, thus improving the output. The following features are experimented with: Latent Dirichlet Allocation (LDA) topic vectors and GMM subspace i-vectors derived from audio. These are contrasted against: skip-gram/Word2Vec features and LDA features derived from text. The results are encouraging and show that acoustic information does help with NMT, leading to an overall 3.3% relative improvement in BLEU scores.

Recurrent neural network language models (RNNLMs) can be augmented with auxiliary features, which can provide an extra modality on top of the words. It has been found that RNNLMs perform best when trained on a large corpus of generic text and then fine-tuned on text corresponding to the sub-domain for which it is to be applied. However, in many cases the auxiliary features are available for the sub-domain text but not for the generic text. In such cases, semi-supervised techniques can be used to infer such features for the generic text data such that the RNNLM can be trained and then fine-tuned on the available in-domain data with corresponding auxiliary features. \n \nIn this paper, several novel approaches are investigated for dealing with the semi-supervised adaptation of RNNLMs with auxiliary features as input. These approaches include: using zero features during training to mask the weights of the feature sub-network; adding the feature sub-network only at the time of fine-tuning; deriving the features using a parametric model and; back-propagating to infer the features on the generic text. These approaches are investigated and results are reported both in terms of PPL and WER on a multi-genre broadcast ASR task.

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.

This paper presents the most recent developments of the \nwebASR service (www.webasr.org), the world’s first web– \nbased fully functioning automatic speech recognition platform \nfor scientific use. Initially released in 2008, the functionalities \nof webASR have recently been expanded with 3 main goals in \nmind: Facilitate access through a RESTful architecture, that allows \nfor easy use through either the web interface or an API; allow \nthe use of input metadata when available by the user to improve \nsystem performance; and increase the coverage of available \nsystems beyond speech recognition. Several new systems \nfor transcription, diarisation, lightly supervised alignment and \ntranslation are currently available through webASR. The results \nin a series of well–known benchmarks (RT’09, IWSLT’12 and \nMGB’15 evaluations) show how these webASR systems provides \nstate–of–the–art performances across these tasks

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.

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.

In this paper, we present a novel approach to speech- driven facial animation using a non-parametric switching state space model based on Gaussian processes. The model is an extension of the shared Gaussian process dynamical model, augmented with switching states. Two talking head corpora are processed by extracting visual and audio data from the sequences followed by a parameterization of both data streams. Phonetic labels are obtained by performing forced phonetic alignment on the audio. The switching states are found using a variable length Markov model trained on the labelled phonetic data. The audio and visual data corresponding to phonemes matching each switching state are extracted and modelled together using a shared Gaussian process dynamical model. We propose a synthesis method that takes into account both previous and future phonetic context, thus accounting for forward and backward coarticulation in speech. Both objective and subjective evaluation results are presented. The quantitative results demonstrate that the proposed method outperforms other state-of-the-art methods in visual speech synthesis and the qualitative results reveal that the synthetic videos are comparable to ground truth in terms of visual perception and intelligibility.