Improving data selection for low-resource STT and KWS

Abstract

This paper extends recent research on training data selection for speech transcription and keyword spotting system development. Selection techniques were explored in the context of the IARPA-Babel Active Learning (AL) task for 6 languages. Different selection criteria were considered with the goal of improving over a system built using a pre-defined 3-hour training data set. Four variants of the entropy-based criterion were explored: words, triphones, phones as well as the use of HMM-states previously introduced in [4]. The influence of the number of HMM-states was assessed as well as whether automatic or manual reference transcripts were used. The combination of selection criteria was investigated, and a novel multi-stage selection method proposed. This method was also assessed using larger data sets than were permitted in the Babel AL task. Results are reported for the 6 languages. The multi-stage selection was also applied to the surprise language (Swahili) in the NIST OpenKWS 2015 evaluation.

Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models

“…This method was used in [6] together with an N-best entropy based data selection. Finally, the study in [7] found that HMM-state entropy and letter density are good indicators of the utterance informativeness. Encouraging results were reported from the early attempts [2,3] with a 60% reduction of the transcription cost over Random Selection (RS).…”

“…In this paper, we focus on conventional confidence-based AL as suggested in [2], although other studies [3,6,7] have shown some improvement over it. It is however worth highlighting that the details of the baseline confidence-based approach were not always clearly described, and that subsequent results were not in line with those reported in [2].…”

“…In most of existing AL and SST studies (e.g. [2,3,6,7,13,14]), the Word Error Rate (WER) typically ranges between 25 and 75%. The baseline model in the present work has a WER of about 12.5%, which makes the application of AL and SST on an industrial task even more challenging.…”

Overcoming Data Sparsity in Acoustic Modeling of Low-Resource Language by Borrowing Data and Model Parameters from High-Resource Languages

“…In many of the previous works [21,22], the use of untranscribed data from the same language to improve the performance of the acoustic model in a low-resource language were studied. However, the use of transcribed data from closely related languages were not studied in detail.…”

Investigating techniques for low resource conversational speech recognition

“…In this case, the automatic transcripts are directly used for acoustic model training. 2) Data selection is also used to get relevant training data [4,5]. In contrast to SST, here the goal is to select data for which accurate manual transcripts will be created.…”