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

The 2005 AMI system for the transcription of speech in meetings

In this paper we describe the 2005 AMI system for the transcription\ud of speech in meetings used for participation in the 2005 NIST\ud RT evaluations. The system was designed for participation in the speech\ud to text part of the evaluations, in particular for transcription of speech\ud recorded with multiple distant microphones and independent headset\ud microphones. System performance was tested on both conference room\ud and lecture style meetings. Although input sources are processed using\ud different front-ends, the recognition process is based on a unified system\ud architecture. The system operates in multiple passes and makes use\ud of state of the art technologies such as discriminative training, vocal\ud tract length normalisation, heteroscedastic linear discriminant analysis,\ud speaker adaptation with maximum likelihood linear regression and minimum\ud word error rate decoding. In this paper we describe the system performance\ud on the official development and test sets for the NIST RT05s\ud evaluations. The system was jointly developed in less than 10 months\ud by a multi-site team and was shown to achieve very competitive performance

Transcription of conference room meetings: an investigation

The automatic processing of speech collected in conference style meetings has attracted considerable interest with several large scale projects devoted to this area. In this paper we explore the use of various meeting corpora for the purpose of automatic speech recognition. In particular we investigate the similarity of these resources and how to efficiently use them in the construction of a meeting transcription system. The analysis shows distinctive features for each resource. However the benefit in pooling data and hence the similarity seems sufficient to speak of a generic conference meeting domain . In this context this paper also presents work on development for the AMI meeting transcription system, a joint effort by seven sites working on the AMI (augmented multi-party interaction) project

Evaluating automatic speaker recognition systems: an overview of the nist speaker recognition evaluations (1996-2014)

2014 CSIC. Manuscripts published in this Journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.Automatic Speaker Recognition systems show interesting properties, such as speed of processing or repeatability of results, in contrast to speaker recognition by humans. But they will be usable just if they are reliable. Testability, or the ability to extensively evaluate the goodness of the speaker detector decisions, becomes then critical. In the last 20 years, the US National Institute of Standards and Technology (NIST) has organized, providing the proper speech data and evaluation protocols, a series of text-independent Speaker Recognition Evaluations (SRE). Those evaluations have become not just a periodical benchmark test, but also a meeting point of a collaborative community of scientists that have been deeply involved in the cycle of evaluations, allowing tremendous progress in a specially complex task where the speaker information is spread across different information levels (acoustic, prosodic, linguistic…) and is strongly affected by speaker intrinsic and extrinsic variability factors. In this paper, we outline how the evaluations progressively challenged the technology including new speaking conditions and sources of variability, and how the scientific community gave answers to those demands. Finally, NIST SREs will be shown to be not free of inconveniences, and future challenges to speaker recognition assessment will also be discussed

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

Combining Spectral Representations for Large Vocabulary Continuous Speech Recognition

In this paper we investigate the combination of complementary acoustic feature streams in large vocabulary continuous speech recognition (LVCSR). We have explored the use of acoustic features obtained using a pitch-synchronous analysis, STRAIGHT, in combination with conventional features such as mel frequency cepstral coefficients. Pitch-synchronous acoustic features are of particular interest when used with vocal tract length normalisation (VTLN) which is known to be affected by the fundamental frequency. We have combined these spectral representations directly at the acoustic feature level using heteroscedastic linear discriminant analysis (HLDA) and at the system level using ROVER. We evaluated this approach on three LVCSR tasks: dictated newspaper text (WSJCAM0), conversational telephone speech (CTS), and multiparty meeting transcription. The CTS and meeting transcription experiments were both evaluated using standard NIST test sets and evaluation protocols. Our results indicate that combining conventional and pitch-synchronous acoustic feature sets using HLDA results in a consistent, significant decrease in word error rate across all three tasks. Combining at the system level using ROVER resulted in a further significant decrease in word error rate