A Bandpass Transform for Speaker Normalization

One of the major challenges for Automatic Speech Recognition is to handle speech variability. Inter-speaker variability is partly due to differences in speakers' anatomy and especially in their Vocal Tract geometry. Dissimilarities in Vocal Tract Length (VTL) are a known source of speech variation. Vocal Tract Length Normalization is a popular Speaker Normalization technique that can be implemented as a transformation of a spectrum frequency axis. We introduce in this document a new spectral transformation for Speaker Normalization. We use the Bilinear Transformation to introduce a new frequency warping resulting from a mapping of a prototype Band-Pass (BP) filter into a general BP filter. This new transformation called the Bandpass Transformation (BPT) offers two degrees of freedom enabling complex warpings of the frequency axis that are different from previous works with the Bilinear Transform. We then define a procedure to use BPT for Speaker Normalization based on the Nelder-Mead algorithm for the estimation of the BPT parameters. We present a detailed study of the performance of our new approach on two test sets with gender dependent and independent systems. Our results demonstrate clear improvements compared to standard methods used in VTL Normalization. A score compensation procedure is presented and results in further improvements of our results by refining our BPT parameter estimation

ALBAYZIN 2018 spoken term detection evaluation: a multi-domain international evaluation in Spanish

[Abstract] Search on speech (SoS) is a challenging area due to the huge amount of information stored in audio and video repositories. Spoken term detection (STD) is an SoS-related task aiming to retrieve data from a speech repository given a textual representation of a search term (which can include one or more words). This paper presents a multi-domain internationally open evaluation for STD in Spanish. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation task aims at retrieving the speech files that contain the terms, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the COREMAH database, which contains 2-people spontaneous speech conversations about different topics. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the results, and detailed post-evaluation analyses based on some term properties (within-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and native/foreign terms). Fusion results of the primary systems submitted to the evaluation are also presented. Three different research groups took part in the evaluation, and 11 different systems were submitted. The obtained results suggest that the STD task is still in progress and performance is highly sensitive to changes in the data domain.Ministerio de Economía y Competitividad; TIN2015-64282-R,Ministerio de Economía y Competitividad; RTI2018-093336-B-C22Ministerio de Economía y Competitividad; TEC2015-65345-PXunta de Galicia; ED431B 2016/035Xunta de Galicia; GPC ED431B 2019/003Xunta de Galicia; GRC 2014/024Xunta de Galicia; ED431G/01Xunta de Galicia; ED431G/04Agrupación estratéxica consolidada; GIU16/68Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-

Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation

[Abstract] The huge amount of information stored in audio and video repositories makes search on speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given a spoken query. Research on this area is continuously fostered with the organization of QbE STD evaluations. This paper presents a multi-domain internationally open evaluation for QbE STD in Spanish. The evaluation aims at retrieving the speech files that contain the queries, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops; RTVE database, which includes broadcast television (TV) shows; and COREMAH database, which contains 2-people spontaneous speech conversations about different topics. The evaluation has been designed carefully so that several analyses of the main results can be carried out. We present the evaluation itself, the three databases, the evaluation metrics, the systems submitted to the evaluation, the results, and the detailed post-evaluation analyses based on some query properties (within-vocabulary/out-of-vocabulary queries, single-word/multi-word queries, and native/foreign queries). Fusion results of the primary systems submitted to the evaluation are also presented. Three different teams took part in the evaluation, and ten different systems were submitted. The results suggest that the QbE STD task is still in progress, and the performance of these systems is highly sensitive to changes in the data domain. Nevertheless, QbE STD strategies are able to outperform text-based STD in unseen data domains.Centro singular de investigación de Galicia; ED431G/04Universidad del País Vasco; GIU16/68Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-PMinisterio de Ciencia, Innovación y Competitividad; RTI2018-098091-B-I00Xunta de Galicia; ED431G/0

A Vectorized Processing Algorithm for Continuous Speech Recognition and Associated FPGA-Based Architecture

This work analyzes Continuous Automatic Speech Recognition (CSR) and in contrast to prior work, it shows that the CSR algorithms can be specified in a highly parallel form. Through use of the MATLAB software package, the parallelism is exploited to create a compact, vectorized algorithm that is able to execute the CSR task. After an in-depth analysis of the SPHINX 3 Large Vocabulary Continuous Speech Recognition (LVCSR) engine the major functional units were redesigned in the MATLAB environment, taking special effort to flatten the algorithms and restructure the data to allow for matrix-based computations. Performing this conversion resulted in reducing the original 14,000 lines of C++ code into less then 200 lines of highly-vectorized operations, substantially increasing the potential Instruction Line Parallelism of the system. Using this vector model as a baseline, a custom hardware system was then created that is capable of performing the speech recognition task in real-time on a Xilinx Virtex-4 FPGA device. Through the creation independent hardware engines for each stage of the speech recognition process, the throughput of each is maximized by customizing the logic to the specific task. Further, a unique architecture was designed that allows for the creation of a static data path throughout the hardware, effectively removing the need for complex bus arbitration in the system. By making using of shared memory resources and applying a token passing scheme to the system, both the data movement within the design as well as the amount of active data are continually minimized during run-time. These results provide a novel method for perform speech recognition in both hardware and software, helping to further the development of systems capable of recognizing human speech