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Found 11 papers, 2 papers with code
JHU IWSLT 2022 Dialect Speech Translation System Description
This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Speech collage: code-switched audio generation by collaging monolingual corpora
Designing effective automatic speech recognition (ASR) systems for Code-Switching (CS) often depends on the availability of the transcribed CS resources.
Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilization
Incorporating longer context has been shown to benefit machine translation, but the inclusion of context in end-to-end speech translation (E2E-ST) remains under-studied.
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition
The pervasiveness of intra-utterance code-switching (CS) in spoken content requires that speech recognition (ASR) systems handle mixed language.
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus
We also report the first baseline for Arabic punctuation restoration.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Arabic Code-Switching Speech Recognition using Monolingual Data
Code-switching in automatic speech recognition (ASR) is an important challenge due to globalization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus
We also report the first baseline for Arabic punctuation restoration.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition
In this paper, we exploit the transfer learning approach to design End-to-End (E2E) CS ASR systems for the two low-resourced language pairs using different monolingual speech data and a small set of noisy CS data.
Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR
We evaluate the system performance handling: (i) monolingual (Ar, En and Fr); (ii) multi-dialectal (Modern Standard Arabic, along with dialectal variation such as Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and dialectal (MSA-Egyptian dialect) test cases, and compare with current state-of-the-art systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Arabic Speech Recognition by End-to-End, Modular Systems and Human
Recent advances in automatic speech recognition (ASR) have achieved accuracy levels comparable to human transcribers, which led researchers to debate if the machine has reached human performance.
