Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Building competitive direct acoustics-to-word models for English conversational speech recognition

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple. Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models. Our work also showed this accuracy gap when using the English Switchboard-Fisher data set. This paper describes a recipe to train an A2W model that closes this gap and is at-par with state-of-the-art sub-word based models. We achieve a word error rate of 8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder or language model. We find that model initialization, training data order, and regularization have the most impact on the A2W model performance. Next, we present a joint word-character A2W model that learns to first spell the word and then recognize it. This model provides a rich output to the user instead of simple word hypotheses, making it especially useful in the case of words unseen or rarely-seen during training.Comment: Submitted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set

A spoken document retrieval application in the oral history domain

The application of automatic speech recognition in the broadcast news domain is well studied. Recognition performance is generally high and accordingly, spoken document retrieval can successfully be applied in this domain, as demonstrated by a number of commercial systems. In other domains, a similar recognition performance is hard to obtain, or even far out of reach, for example due to lack of suitable training material. This is a serious impediment for the successful application of spoken document retrieval techniques for other data then news. This paper outlines our first steps towards a retrieval system that can automatically be adapted to new domains. We discuss our experience with a recently implemented spoken document retrieval application attached to a web-portal that aims at the disclosure of a multimedia data collection in the oral history domain. The paper illustrates that simply deploying an off-theshelf\ud broadcast news system in this task domain will produce error rates that are too high to be useful for retrieval tasks. By applying adaptation techniques on the acoustic level and language model level, system performance can be improved considerably, but additional research on unsupervised adaptation and search interfaces is required to create an adequate search environment based on speech transcripts

Subword and Crossword Units for CTC Acoustic Models

This paper proposes a novel approach to create an unit set for CTC based speech recognition systems. By using Byte Pair Encoding we learn an unit set of an arbitrary size on a given training text. In contrast to using characters or words as units this allows us to find a good trade-off between the size of our unit set and the available training data. We evaluate both Crossword units, that may span multiple word, and Subword units. By combining this approach with decoding methods using a separate language model we are able to achieve state of the art results for grapheme based CTC systems.Comment: Current version accepted at Interspeech 201

Advances in All-Neural Speech Recognition

This paper advances the design of CTC-based all-neural (or end-to-end) speech recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in which a second system is used to transform a noisy initial output into a cleaner version. We present a number of stabilization and initialization methods we have found useful in training these networks. We evaluate our system on the commonly used NIST 2000 conversational telephony test set, and significantly exceed the previously published performance of similar systems, both with and without the use of an external language model and decoding technology

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task

Examining the contributions of automatic speech transcriptions and metadata sources for searching spontaneous conversational speech

The searching spontaneous speech can be enhanced by combining automatic speech transcriptions with semantically related metadata. An important question is what can be expected from search of such transcriptions and different sources of related metadata in terms of retrieval effectiveness. The Cross-Language Speech Retrieval (CL-SR) track at recent CLEF workshops provides a spontaneous speech test collection with manual and automatically derived metadata fields. Using this collection we investigate the comparative search effectiveness of individual fields comprising automated transcriptions and the available metadata. A further important question is how transcriptions and metadata should be combined for the greatest benefit to search accuracy. We compare simple field merging of individual fields with the extended BM25 model for weighted field combination (BM25F). Results indicate that BM25F can produce improved search accuracy, but that it is currently important to set its parameters suitably using a suitable training set