Search Results for author:
Found 36 papers, 9 papers with code
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation
Documentation of endangered languages (ELs) has become increasingly urgent as thousands of languages are on the verge of disappearing by the end of the 21st century.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
CMU’s IWSLT 2022 Dialect Speech Translation System
We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.
Multimodal Modeling For Spoken Language Identification
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance.
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation
To solve this problem, we would like to simultaneously generate automatic speech recognition (ASR) and ST predictions such that each source language word is explicitly mapped to a target language word.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation.
CTC Alignments Improve Autoregressive Translation
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Two-Pass Low Latency End-to-End Spoken Language Understanding
End-to-end (E2E) models are becoming increasingly popular for spoken language understanding (SLU) systems and are beginning to achieve competitive performance to pipeline-based approaches.
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
Conformer has proven to be effective in many speech processing tasks.
LegoNN: Building Modular Encoder-Decoder Models
We describe LegoNN, a procedure for building encoder-decoder architectures in a way so that its parts can be applied to other tasks without the need for any fine-tuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type.
Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates
We propose Fast-MD, a fast MD model that generates HI by non-autoregressive (NAR) decoding based on connectionist temporal classification (CTC) outputs followed by an ASR decoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Differentiable Allophone Graphs for Language-Universal Speech Recognition
These phone-based systems with learned allophone graphs can be used by linguists to document new languages, build phone-based lexicons that capture rich pronunciation variations, and re-evaluate the allophone mappings of seen language.
ESPnet-ST IWSLT 2021 Offline Speech Translation System
This year we made various efforts on training data, architecture, and audio segmentation.
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding
Our splits identify performance gaps up to 10% between end-to-end systems that were within 1% of each other on the original test sets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks.
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system.
Transformer-Transducers for Code-Switched Speech Recognition
We live in a world where 60% of the population can speak two or more languages fluently.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On Long-Tailed Phenomena in Neural Machine Translation
State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens, tackling which remains a major challenge.
Towards Zero-shot Learning for Automatic Phonemic Transcription
The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes.
Universal Phone Recognition with a Multilingual Allophone System
Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages.
Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models
Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability.
Multilingual Speech Recognition with Corpus Relatedness Sampling
For example, the target corpus might benefit more from a corpus in the same domain or a corpus from a close language.
SANTLR: Speech Annotation Toolkit for Low Resource Languages
In this work, we present SANTLR: Speech Annotation Toolkit for Low Resource Languages.
Cross-Attention End-to-End ASR for Two-Party Conversations
We present an end-to-end speech recognition model that learns interaction between two speakers based on the turn-changing information.
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Phoneme Level Language Models for Sequence Based Low Resource ASR
Building multilingual and crosslingual models help bring different languages together in a language universal space.
Zero-shot Learning for Speech Recognition with Universal Phonetic Model
Our model is able to recognize unseen phonemes in the target language, if only a small text corpus is available.
Domain Robust Feature Extraction for Rapid Low Resource ASR Development
We demonstrate the effectiveness of using a pre-trained English recognizer, which is robust to such mismatched conditions, as a domain normalizing feature extractor on a low resource language.
Sequence-based Multi-lingual Low Resource Speech Recognition
Techniques for multi-lingual and cross-lingual speech recognition can help in low resource scenarios, to bootstrap systems and enable analysis of new languages and domains.
An Approach for Self-Training Audio Event Detectors Using Web Data
The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube.
