# Cumulative Adaptation for BLSTM Acoustic Models

arXiv: https://arxiv.org/abs/1906.06207 · HTML: https://mlsanity.com/paper/1906.06207/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [On the limit of English conversational speech recognition](https://mlsanity.com/paper/2105.00982/) (2021)
- [Conformer-based Hybrid ASR System for Switchboard Dataset](https://mlsanity.com/paper/2111.03442/) (2021)
- [Improving the Training Recipe for a Robust Conformer-based Hybrid Model](https://mlsanity.com/paper/2206.12955/) (2022)
- [On Architectures and Training for Raw Waveform Feature Extraction in ASR](https://mlsanity.com/paper/2104.04298/) (2021)
- [Enhancing and Adversarial: Improve ASR with Speaker Labels](https://mlsanity.com/paper/2211.06369/) (2022)
- [Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems](https://mlsanity.com/paper/2206.11596/) (2022)
- [On Sampling-Based Training Criteria for Neural Language Modeling](https://mlsanity.com/paper/2104.10507/) (2021)
- [Self-Normalized Importance Sampling for Neural Language Modeling](https://mlsanity.com/paper/2111.06310/) (2021)
- [Analyzing And Improving Neural Speaker Embeddings for ASR](https://mlsanity.com/paper/2301.04571/) (2023)
- [Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems](https://mlsanity.com/paper/2306.14608/) (2023)

## Abstract (arXiv)

This paper addresses the robust speech recognition problem as an adaptation task. Specifically, we investigate the cumulative application of adaptation methods. A bidirectional Long Short-Term Memory (BLSTM) based neural network, capable of learning temporal relationships and translation invariant representations, is used for robust acoustic modelling. Further, i-vectors were used as an input to the neural network to perform instantaneous speaker and environment adaptation, providing 8% relative improvement in word error rate on the NIST Hub5 2000 evaluation test set. By enhancing the first-pass i-vector based adaptation with a second-pass adaptation using speaker and environment dependent transformations within the network, a further relative improvement of 5% in word error rate was achieved. We have reevaluated the features used to estimate i-vectors and their normalization to achieve the best performance in a modern large scale automatic speech recognition system.