In this paper, we give an overview of the AMIDA systems for transcription of conference and lecture room meetings. The systems were developed for participation in the Rich Transcription evaluations conducted by the National Institute for Standards and Technology in the years 2007 and 2009 and can process close talking and far field microphone recordings. The paper first discusses fundamental properties of meeting data with special focus on the AMI/AMIDA corpora. This is followed by a description and analysis of improved processing and modeling, with focus on techniques specifically addressing meeting transcription issues such as multi-room recordings or domain variability. In 2007 and 2009, two different strategies of systems building were followed. While in 2007 we used our traditional style system design based on cross adaptation, the 2009 systems were constructed semi-automatically, supported by improved decoders and a new method for system representation. Overall these changes gave a 6%-13% relative reduction in word error rate compared to our 2007 results while at the same time requiring less training material and reducing the real-time factor by five times. The meeting transcription systems are available at www.webasr.org.

Most current very low bit rate (VLBR) speech coding systems use hidden Markov model (HMM) based speech recognition and synthesis techniques. This allows transmission of information (such as phonemes) segment by segment; this decreases the bit rate. However, an encoder based on a phoneme speech recognition may create bursts of segmental errors; these would be further propagated to any suprasegmental (such as syllable) information coding. Together with the errors of voicing detection in pitch parametrization, HMM-based speech coding leads to speech discontinuities and unnatural speech sound artifacts. In this paper, we propose a novel VLBR speech coding framework based on neural networks (NNs) for end-to-end speech analysis and synthesis without HMMs. The speech coding framework relies on a phonological (subphonetic) representation of speech. It is designed as a composition of deep and spiking NNs: a bank of phonological analyzers at the transmitter, and a phonological synthesizer at the receiver. These are both realized as deep NNs, along with a spiking NN as an incremental and robust encoder of syllable boundaries for coding of continuous fundamental frequency (F0). A combination of phonological features defines much more sound patterns than phonetic features defined by HMM-based speech coders; this finer analysis/synthesis code contributes to smoother encoded speech. Listeners significantly prefer the NN-based approach due to fewer discontinuities and speech artifacts of the encoded speech. A single forward pass is required during the speech encoding and decoding. The proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s.

Recent work in text to speech synthesis has pointed to the benefit of using a continuous pitch estimate; that is, one that records pitch even when voicing is not present. Such an approach typically requires interpolation. The purpose of this letter is to show that a continuous pitch estimation is available from a combination of otherwise well known techniques. Further, in the case of an autocorrelation based estimate, the continuous requirement negates the need for other heuristics to correct for common errors. An algorithm is suggested, illustrated, and demonstrated using a parametric vocoder.

Two of the major challenges in microphone array based adaptive beamforming, speech enhancement and distant speech recognition, are robust and accurate source localization and voice activity detection. This paper introduces a spatial gradient steered response power using the phase transform (SRP-PHAT) method which is capable of localization of competing speakers in overlapping conditions. We further investigate the behavior of the SRP function and characterize theoretically a fixed point in its search space for the diffuse noise field. We call this fixed point the null position in the SRP search space. Building on this evidence, we propose a technique for multichannel voice activity detection (MVAD) based on detection of a maximum power corresponding to the null position. The gradient SRP-PHAT in tandem with the MVAD form an integrated framework of multi-source localization and voice activity detection. The experiments carried out on real data recordings show that this framework is very effective in practical applications of hands-free communication.