Data utility modelling for mismatch reduction

In the "missing data" (MD) approach to noise robust automatic speech recognition (ASR), speech models are trained on clean data, and during recognition sections of spectral data dominated by noise are detected and treated as "missing". However, this all-or-nothing hard decision about which data is missing does not accurately reflect the probabilistic nature of missing data detection. Recent work has shown greatly improved performance by the "soft missing data" (SMD) approach, in which the "missing" status of each data value is represented by a continuous probability rather than a 0/1 value. This probability is then used to weight between the different likelihood contributions which the MD model normally assigns to each spectral observation according to its "missing" status. This article presents an analysis which shows that the SMD approach effectively implements a Maximum A-Posteriori (MAP) decoding strategy with missing or uncertain data, subject to the interpretation that the missing/not-missing probabilities are weights for a mixture pdf which models the pdf for each hidden clean data input, after conditioning by the noisy data input, a local noise estimate, and any information which may be available. An important feature of this "soft data" model is that control over the "evidence pdf" can provide a principled framework not only for ignoring unreliable data, but also for focusing attention on more discriminative features, and for data enhancement

Data utility modelling for mismatch reduction

In the "missing data" (MD) approach to noise robust automatic speech recognition (ASR), speech models are trained on clean data, and during recognition sections of spectral data dominated by noise are detected and treated as "missing". However, this all-or-nothing hard decision about which data is missing does not accurately reflect the probabilistic nature of missing data detection. Recent work has shown greatly improved performance by the "soft missing data" (SMD) approach, in which the "missing" status of each data value is represented by a continuous probability rather than a 0/1 value. This probability is then used to weight between the different likelihood contributions which the MD model normally assigns to each spectral observation according to its "missing" status. This article presents an analysis which shows that the SMD approach effectively implements a Maximum A-Posteriori (MAP) decoding strategy with missing or uncertain data, subject to the interpretation that the missing/not-missing probabilities are weights for a mixture pdf which models the pdf for each hidden clean data input, after conditioning by the noisy data input, a local noise estimate, and any information which may be available. An important feature of this "soft data" model is that control over the "evidence pdf" can provide a principled framework not only for ignoring unreliable data, but also for focusing attention on more discriminative features, and for data enhancement

Identifying Cover Songs Using Information-Theoretic Measures of Similarity

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/This paper investigates methods for quantifying similarity between audio signals, specifically for the task of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantized audio features, to continuous-valued approaches. In the discrete case, we propose a method for computing the normalized compression distance, where we account for correlation between time series. In the continuous case, we propose to compute information-based measures of similarity as statistics of the prediction error between time series. We evaluate our methods on two cover song identification tasks using a data set comprised of 300 Jazz standards and using the Million Song Dataset. For both datasets, we observe that continuous-valued approaches outperform discrete-valued approaches. We consider approaches to estimating the normalized compression distance (NCD) based on string compression and prediction, where we observe that our proposed normalized compression distance with alignment (NCDA) improves average performance over NCD, for sequential compression algorithms. Finally, we demonstrate that continuous-valued distances may be combined to improve performance with respect to baseline approaches. Using a large-scale filter-and-refine approach, we demonstrate state-of-the-art performance for cover song identification using the Million Song Dataset.The work of P. Foster was supported by an Engineering and Physical Sciences Research Council Doctoral Training Account studentship

Exploiting correlogram structure for robust speech recognition with multiple speech sources

This paper addresses the problem of separating and recognising speech in a monaural acoustic mixture with the presence of competing speech sources. The proposed system treats sound source separation and speech recognition as tightly coupled processes. In the first stage sound source separation is performed in the correlogram domain. For periodic sounds, the correlogram exhibits symmetric tree-like structures whose stems are located on the delay that corresponds to multiple pitch periods. These pitch-related structures are exploited in the study to group spectral components at each time frame. Local pitch estimates are then computed for each spectral group and are used to form simultaneous pitch tracks for temporal integration. These processes segregate a spectral representation of the acoustic mixture into several time-frequency regions such that the energy in each region is likely to have originated from a single periodic sound source. The identified time-frequency regions, together with the spectral representation, are employed by a `speech fragment decoder' which employs `missing data' techniques with clean speech models to simultaneously search for the acoustic evidence that best matches model sequences. The paper presents evaluations based on artificially mixed simultaneous speech utterances. A coherence-measuring experiment is first reported which quantifies the consistency of the identified fragments with a single source. The system is then evaluated in a speech recognition task and compared to a conventional fragment generation approach. Results show that the proposed system produces more coherent fragments over different conditions, which results in significantly better recognition accuracy