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Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

Hamza Kheddar, Yassine Himeur, Somaya Al-Maadeed, Abbes Amira, Faycal Bensaali

arXiv:2304.14535v2cs.SDcs.CLcs.LGeess.AS

TL;DR

DL-based ASR faces data scarcity, resource demands, and mismatches between training and test data. This review surveys DTL-based ASR frameworks, organizing methods and examining their limitations and applications. It finds DTL-based ASR effective across varied scenarios, with computational efficiency and potential advantages over existing ML algorithms, especially when target samples differ from training data.

  • Problem

    DL-based ASR requires substantial data and computational resources, and its same-domain training and test assumption does not hold in some real-world applications.

    Source: §1.1 · p. 1

  • Method

    The paper surveys DTL-based ASR frameworks, classifies them through a taxonomy, and critically analyzes their limitations, applications, and challenges.

    Source: §7 · p. 27

  • Results

    The reviewed DTL-based ASR methods are effective across varied scenarios and can outperform existing ML algorithms, especially when target samples diverge from training data.

    Source: §5 · p. 23

  • Takeaways & Limitations

    DTL has shown less computational, data, and label dependence in some scenarios and better performance in some case studies, but further research is needed to improve generalizability and overall performance.

    Source: §7 · p. 27

    Knowledge-gain metrics can be difficult to predict across DTL techniques, particularly when ASR tasks have different class sets.

    Source: §5.4 · p. 24

Abstract

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Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research.

1. Introduction

The introduction motivates DTL as a response to ASR’s data and domain-mismatch constraints, then presents a review of DTL-based ASR that classifies and critically compares existing frameworks.

1.1. Preliminary

  • DL-based ASR needs large training datasets and substantial computational and storage resources, while data scarcity and train–test mismatch can impair performance.The mismatch concerns domain, input feature space, and data-distribution characteristics.Source: §1.1 · p. 1
  • DTL transfers knowledge from source domains or tasks to target ones, including by reusing pretrained models to address small datasets and distribution inconsistency.The introduction says this can also reduce overfitting and speed progress on the target task.Source: §1.1 · pp. 1–2

1.2. Contribution of the paper

  • The review covers DTL-based ASR frameworks published from 2015 to 2023 and introduces a taxonomy based on transferred knowledge, label availability, and strategy.It also examines frameworks’ limitations and compares their performance.Source: §1.2 · p. 2
  • The survey describes evaluation metrics and datasets used to validate DTL-based ASR schemes.Source: §1.2 · p. 2
  • Its taxonomy organizes DTL-based ASR methodologies with regard to acoustic-model and language-model domains.Source: §1.2 · p. 2
  • The review investigates adversarial DTL-based ASR methods and summarizes domain-adaptation techniques used in ASR applications.Source: §1.2 · p. 3
  • It relates DTL-based ASR to fields including speech translation, speech evaluation, and medical diagnosis.Source: §1.2 · p. 3
  • The survey identifies challenges including negative transfer, knowledge-gain measurement, and DTL unification, and suggests future research directions.Source: §1.2 · p. 3

2. Review methodology

The review searches and screens DTL-based ASR studies, then quantifies publication patterns. Its selection protocol combines keyword matching, innovation and impact, and a 2015–2023 publication window.

2.1. Literature search strategy

  • The review searched Scopus, Web of Science, Elsevier, IEEE, ACM, Wiley, and IET Digital Library for relevant studies.Source: §2.1 · p. 3

2.2. Selection study

  • Study selection used manually grouped keyword matches, innovation and impact criteria, and novelty; it excluded repetitive or non-English work and covered 2015–2023.Table 2 summarizes the number of included papers per database.Source: §2.2 · p. 3

2.3. Quantitative analysis

  • DTL-based ASR studies increased yearly, and accounted for 17% of all ASR studies in the Scopus analysis.Figure 2 also shows keyword-search counts and comparisons; the keyword searches overlap.Source: Figure 2 · p. 4, §2.3 · p. 4

3. Overview of DTL techniques for speech recognition

This section overviews DTL techniques for speech recognition, organizing approaches by transfer settings and outlining ASR system components and evaluation criteria. It also describes cross-validation for estimating generalizability and surveys publicly available datasets used in DTL-based ASR applications.

3.1. DTL conceptual background

  • DTL uses learned knowledge from a source domain and task to improve a target predictive function when the domains or tasks differ.A popular approach reduces differences between domains or tasks to preserve similarity between feature or label spaces.Source: §3.1 · p. 5

3.2. Taxonomy of existing DTL techniques

  • DTL has no standardized taxonomy; one scheme groups methods by label availability and modality, then by transfer of instances, features, parameters, or relations.Its top-level groups are inductive, transductive, cross-modality, and unsupervised DTL.Source: §3.2 · p. 5

3.2.1. Inductive DTL

  • Inductive DTL enhances target prediction across distinct tasks, using multi-task learning with labeled source data or sequential learning with unlabeled source and labeled target data.Multi-task learning trains source and target activities in parallel; sequential learning transfers representations in stages and is slower, but can help when tasks are not all available during training. Sequential methods include fine-tuning, parameter-efficient adapters, feature-based transfer, and zero-shot transfer without parameter optimization.Source: §3.2.1 · pp. 5–6

3.2.2. Transductive DTL

  • Transductive DTL uses labeled source data to build a target predictor for an unlabeled target domain with the same task; its settings include domain adaptation, cross-modality, and unsupervised transfer.Domain adaptation assumes identical feature spaces but differing input distributions; cross-modality allows distinct feature and label spaces, while unsupervised DTL transfers between related but different tasks without visible labels.Source: §3.2.2 · pp. 6–7

3.2.3. Adversarial DTL

  • Adversarial DTL learns transferable, discriminative representations; DANN uses domain-adversarial loss, while DDA dynamically weights marginal and conditional distributions.DANN-based domain-adversarial training was subsequently used in.Source: §3.2.3 · p. 7

3.3.1. Structure of ASR systems

  • A conventional ASR pipeline extracts speech features, then uses acoustic and language models, a lexicon, and a decoder to produce candidate word sequences.Feature extraction can include MFCC or filter-bank analysis; the decoder searches for likely sequences and generates an n-best list.Source: §3.3.1 · p. 7

3.3.2. Evaluation criteria in ASR

  • ASR evaluation includes WER, character- or phoneme-level error rates, classification metrics, speech-quality measures, and processing-speed metrics; WER is common but has comparison-scale and noise-related limitations.CER and PER adapt error rate to character and phoneme units; MOS and PESQ assess speech quality, while RTF and APT measure processing speed.Source: §3.3.2 · pp. 8–10
  • DTL generalizability can be assessed by comparison with a target-task scratch baseline, domain-discrepancy measures, convergence, stability across splits, and ablation studies.MMD and KLD are cited as domain-similarity measures; faster convergence, lower performance variance, and ablation results help assess generalizability.Source: §3.3.2 · p. 10
  • Cross-validation averages performance across folds whose training and validation data remain independent, providing a more robust estimate of generalizability.The target data are split into multiple folds, with the model trained and evaluated on each fold.Source: §3.3.2 · p. 10

3.3.3. ASR datasets

  • The survey catalogs publicly available datasets used for DTL-based ASR, noting that many have been updated and developed over time.The datasets are listed with their characteristics.Source: Table 4 · p. 11

4. DTL-based ASR applications

The section situates DTL-based ASR across language- and acoustic-model approaches, then surveys adaptation frameworks addressing domain shift and low-resource settings.

4. DTL-based ASR applications

  • ASR spans language and acoustic models: BERT is discussed for language modeling, while DNN-HMM, CTC, and Seq2Seq represent acoustic-model approaches.The passage describes CTC as end-to-end training without data pre-alignment and Seq2Seq as recognizing speech without a language model or pronunciation dictionary.Source: §4 · p. 11
  • DA with encoder layers, embeddings, SpecAug, and multi-task CTC reports approximately 40% average relative improvement over baseline; DRAFT-fine-tuned HuBERT performs best on OGI.The survey attributes the improvement to and the OGI result to.Source: §4 · p. 11
  • Adapters can reduce parameters in pretrained upstream models for task-specific adaptation to downstream ASR tasks.Source: §4 · p. 12
  • Figure 6 summarizes existing DTL-based ASR subcategories and their most-used models.Source: Figure 6 · p. 12, §4 · p. 12

4.1. DTL for acoustic models (AMs)

Acoustic-model DTL adapts speech recognition models through feature transformations, conservative training, and parameter subspaces. These strategies address adaptation with limited speech data while preserving or reusing source-model knowledge.

4.1. DTL for acoustic models (AMs)

  • Acoustic models include layered DNN-HMM systems, which map acoustic features to context-dependent lexical units, and end-to-end DNNs, which directly produce recognition outputs.End-to-end models can use recurrent layers and CTC loss.Source: §4.1 · p. 12

4.1.1. Feature normalisation based-DTL

  • Feature-normalisation DTL adds a transformation layer, often with a bottleneck last hidden layer, to linearly map speech characteristics.The transformation can be placed at the input or output; a transformed last-hidden-layer representation can yield adapted model parameters.Source: §4.1.1 · p. 12
  • Linear-transformation studies include pronunciation teaching, denoising-autoencoder and DNN optimization, and augmentation methods for data scarcity.The reviewed augmentation approach combines spectral distortion, spectrogram augmentation, and perturbation-based domain data augmentation.Source: §4.1.1 · pp. 12–13

4.1.2. Conservative training for DTL

  • Conservative training uses limited target speech for adaptation while regularizing the target model’s output distributions toward the source model’s distributions.KLD regularization is described as helping prevent overfitting and keeping the adapted model close to the source domain.Source: §4.1.2 · p. 13

4.1.3. Subspace-based DTL

  • Subspace-based DTL represents model parameters or transformations in a subspace, with PCA, SVD, and NMF used for dimensionality reduction.The review notes that omitting matrix components in SVD can cause losses, while NMF’s nonnegativity constraint limits the coefficient matrix but can aid interpretability.Source: §4.1.3 · p. 14
  • CNMF has been used to extract high-level features, followed by DTL on high- and low-level features through multilingual training and multi-task learning.The review describes the resulting performance as comparable to state-of-the-art speech ASR studies.Source: §4.1.3 · p. 14

4.2. DTL for language models (LMs)

The review describes language-model DTL through interpolation and adaptations of neural, topic-based, and pretrained language models. The approaches transfer language knowledge to new domains or tasks, with different methods addressing feature adaptation, fine-tuning, and longer context.

4.2. DTL for language models (LMs)

  • Language-model adaptation seeks to narrow the gap between source and target domains, and backoff n-gram models can combine teacher and student language models by linear interpolation.The interpolation balances teacher and student model weights and can increase n-gram probabilities while maintaining broad general-purpose coverage.Source: §4.2 · p. 14

4.2.1. BERT-based DTL

  • BERT-based approaches transfer pretrained language-model knowledge to ASR rescoring or end-to-end recognition, including multi-task learning with auxiliary word-embedding regression.The reported LibriSpeech experiments found a further WER reduction from a strong baseline with a pretrained encoder, though the advantage was relatively small.Source: §4.2.1 · p. 16

4.2.2. LDA-based DTL

  • LDA-based DTL uses topic features to model document-specific language, including transforming LDA features through a linear layer before using them as LHN input features.LDA is described as a hierarchical Bayesian model in which documents are mixtures over topics and topics are distributions over words.Source: §4.2.2 · p. 16

4.2.3. NNLM-based DTL

  • Neural language-model adaptation can be model-based, such as fine-tuning or added network layers, or feature-based, by augmenting inputs with auxiliary features.Reviewed examples include RNN-LM adaptation and a TDNN-F trained with cross-entropy and LF-MMI objectives for English child speech recognition.Source: §4.2.3 · p. 16

4.2.4. LSTM-based DTL

  • LSTM language models can exploit variable-length sequences and long-range dependencies without n-gram approximation, while long-span models are relevant to conversation and meeting transcription.The review notes that sentence-level training remains common partly because extended-context data can be difficult to gather and may not help next-word prediction in conventional ASR.Source: §4.2.4 · p. 17

4.2.5. DTL-based sequence-to-sequence model

  • Seq2seq ASR uses an RNN encoder to produce embeddings and an RNN decoder that generates outputs using those embeddings and prior predictions.The review also describes multi-task hybrid training of CTC and encoder-decoder designs as an approach investigated for improving overall model performance.Source: §4.2.5 · p. 17, Figure 10 · p. 17

4.3. Cross-domain ASR

Cross-domain ASR applications in the review include emotion recognition, cross-language transfer, and cross-corpus emotion recognition. Their methods adapt representations or learn shared subspaces to address differences between domains, languages, or annotation settings.

4.3.1. DTL-based ASR for emotion recognition

  • DTL for emotion recognition reuses speech-recognition or speaker-recognition representations and combines speech-derived features with smaller datasets annotated for emotional dimensions.One approach estimates valence and arousal from features learned for ASR, enabling use of transcribed speech alongside emotion-annotated data.Source: §4.3.1 · p. 17

4.3.2. Cross-language DTL

  • Cross-language DTL transfers ASR knowledge from high-resource languages to low-resource languages, often assuming phoneme features are shared across languages.Reviewed strategies include fine-tuning, progressive neural networks, and cross-lingual bottleneck features.Source: §4.3.2 · pp. 17–18

4.3.3. Cross-corpus SER (CC-SER)

  • Cross-corpus SER methods learn shared or projected feature subspaces to address differences between source and target corpora, including settings where target data are unlabeled.Methods include domain-adaptive subspace learning, transfer subspace learning, and NMF-based approaches that seek to reduce distribution differences.Source: §4.3.3 · p. 18
  • Other CC-SER approaches use sparse discriminative subspaces, distribution-matching criteria, regression, or label graphs to transfer information across corpora.The review also describes studies of data complementation and augmentation across six emotional-speech corpora spanning speaker and emotion-style variations.Source: §4.3.3 · pp. 18–19

4.4. Adversarial TL-based ASR

Language-adversarial transfer seeks language-invariant features for ASR transfer to an unknown language. The review also describes its combination with semi-supervised training and sequence-distribution matching approaches for domain adaptation.

4.4. Adversarial TL-based ASR

  • Language-adversarial transfer adds a language discriminator to shared hidden layers, with a gradient reversal layer encouraging similar feature distributions across languages.The resulting shared layers can be transferred to a target model for an unknown language.Source: §4.4 · p. 19, Figure 11 · p. 19
  • A low-resource ASR approach combines semi-supervised training with language-adversarial transfer.For accent mismatch, sequence pooling outperformed an MMD-based approach and slightly improved a domain-adversarial-training approach; combined features reduced WER up to 14.72% over the source-domain model.Source: §4.4 · p. 19

4.5. DTL-based ASR for medical diagnosis

The survey reviews DTL-based speech applications in medical diagnosis, including heart-sound classification, Parkinson disease detection, and other health-related tasks. It describes methods spanning pretrained-model adaptation, feature transfer, and ASR-derived representations.

4.5. DTL-based ASR for medical diagnosis

  • DTL-based medical applications include heart-sound classification, Parkinson disease detection, and other diagnostic tasks.The section also summarizes these areas in Table 8.Source: §4.5 · p. 19, Table 8 · p. 21

4.5.1. Heart sound classification

  • A Pascal-dataset heart-sound benchmark was proposed without denoising or cleaning to support more comparable cardiovascular-recognition research.The review also describes a pretrained PANNs-based model for heart-sound classification.Source: §4.5.1 · p. 20

4.5.2. Parkinson disease detection

  • Parkinson-detection approaches include fine-tuned pretrained CNNs, unsupervised two-step sparse transfer learning, and an LSTM-based scheme using spectrogram masking.TSTL is presented as addressing scarce speech data and subject-distribution inconsistency by extracting information from unlabeled speech, aligning train/test distributions, and preserving sample structure.Source: §4.5.2 · p. 20

4.5.3. Other medical diagnosis

  • Other medical applications transfer encoder weights for depression prediction, adapt ASR models for dysarthric speech, and use ASR-derived features for health-state identification.For low-resource-language radiology reports, transformer fine-tuning used a 30-hour in-domain speech corpus and over 1GB of text; reported individual-report WER ranged from 20% to 60%.Source: §4.5.3 · p. 20, Table 8 · p. 21

4.6. DTL-based ASR attacks and security

The section distinguishes adversarial attacks that improve or secure ASR from those that threaten its effectiveness. It describes adversarial examples as perturbations that can alter recognition and notes that DTL can make attacks transferable from source to target models.

4.6. DTL-based ASR attacks and security

  • Adversarial examples use small perturbations to speech or audio features and can cause ASR to recognize an incorrect utterance; DTL may let source-targeted examples attack target models.The section groups DTL-based attacks by whether they improve or threaten ASR effectiveness.Source: §4.6 · p. 21, Figure 12 · p. 21

4.6.1. Positive adversarial attacks

  • Positive adversarial methods combine natural and adversarial data to train a more robust acoustic model using teacher/student training.The reviewed scheme generated adversarial MFCC features with a gradient-sign method and was evaluated on CHiME-4 and Aurora-4 using a customized CNN.Source: §4.6.1 · pp. 21–22

4.6.2. Negative adversarial attacks

  • Negative attacks seek to decrease ASR effectiveness; one reviewed framework used Google Phone as the source model and DeepSpeech 1 as the target.The section also distinguishes white-box from black-box knowledge and targeted from non-targeted objectives.Source: §4.6.2 · p. 22

4.7. DTL-based ASR for other applications

The survey reviews DTL beyond its core ASR applications, including audio and event recognition, speech translation, speaker verification, medical-related speech tasks, and other recognition settings.

Other applications

  • DTL applications beyond core ASR include audio tagging, sound-event detection, speech enhancement, and social-speech evaluation.The reviewed approaches use transferred auditory and visual features, teacher/student adaptation, or transferred models for audio segmentation.Source: §4.7 · p. 22
  • Self-supervised pretraining and cross-lingual transfer are reviewed for speech translation, while DTL is also applied to short-utterance speaker verification and marine-mammal sound classification.The survey describes hierarchical transfer for multilingual text-to-speech in low-resource languages.Source: §4.7 · pp. 22–23
  • PhyAug transforms source training data into augmented target-domain data using domain-shift physics and reduced WER by up to 70% across tested microphones.The review also describes a two-stage pretrained-CNN and machine-learning approach achieving up to 99.9% accuracy for stone classification.Source: §4.7 · p. 23
  • Intermediate CTC-layer distillation reduced target-model WER to 6.24% on dev-clean and 6.3% on test-clean in LibriSpeech.The survey also reports a hierarchical distillation method that reduced relative WER by 9%.Source: §4.7 · p. 23
  • A CNN-audio and RNN-text DTL framework for filled-pause detection achieved 73.9% F-score on an internal dataset and 82.3% on Switchboard with fusion.The review also covers DTL with fuzzy-integral ensembles for musical-instrument classification, reporting testing accuracies of 96% on IRMAS and 98% on PCMIR.Source: §4.7 · p. 23

5. Discussion of key challenges

The discussion describes DTL-based ASR as effective and computationally efficient across applications, while identifying transfer, evaluation, and standardization challenges. It highlights conditions that can constrain generalization and fair comparison.

5. Discussion of key challenges

  • DTL-based ASR methods are described as computationally efficient and effective across applications, especially when target samples differ substantially from training samples.The review says these methods can outperform existing ML algorithms.Source: §5 · p. 23

5.1. The problem of NT

  • Insufficient source-target similarity can limit DTL benefits or cause negative transfer, and source-sample selection is often underreported.The review notes that source-target discrepancy and transferred inductive bias can reduce target-task performance; similarity-based selection is discussed as one way to mitigate negative transfer across domains.Source: §5 · p. 23, §5.1 · p. 24

5.2. The problem of overfitting

  • DTL can still overfit source-domain noise, while choosing layers to remove is computationally costly and may alter dense-layer parameters.The review lists dropout, LASSO, cross-validation, and data augmentation as possible ways to partially alleviate overfitting.Source: §5.2 · p. 24

5.3. Reproducibility of DTL-based ASR results

  • Private customized datasets, few open benchmark datasets, and varied evaluation metrics make fair empirical comparisons difficult or sometimes impossible.The review also notes the absence of an online platform integrating prior DTL-based ASR algorithms and datasets.Source: §5.3 · p. 24

5.4. Measuring knowledge gains

  • Knowledge-gain measurement remains underexplored, and proposed transfer metrics can be difficult to predict for ASR tasks with different class sets.The four metrics proposed in are transfer ratio, transfer loss, transfer error, and in-domain ratio; a perfect baseline can yield indefinite performance results.Source: §5.4 · p. 24

5.5. Unification of DTL

  • Different mathematical formulations, definitions, and terminology across DTL studies motivate further standardization.The review notes an existing attempt to unify DTL definitions and formulations, while stating that further work is needed.Source: §5.5 · p. 25

5.6. Other challenges

  • Speech DTL faces source-target discrepancies involving language, speaker, age, ethnicity, and acoustic surroundings; low-resource settings and unclean data can remain difficult.Other challenges include CTC's frame-independence assumption, defining consistent shared knowledge across languages, and added computational expense in deep domain adaptation.Source: §5.6 · p. 25

6. Future directions in DTL-based ASR

The future directions address transfer across mismatched domains, privacy and interpretability, online adaptation, and ChatGPT-based ASR modeling and assessment.

6.1. Overcoming NT and measuring the cross-domain transferability

  • Cross-domain transfer research should address negative transfer and measure transferability, with transfer choices guided by domain similarity.The review suggests concatenating source and target data for high similarity, using negative-transfer mitigation for medium similarity, and distant or no transfer for low similarity.Source: §6.1 · p. 25

6.2. Privacy preservation

  • Online DTL-based ASR raises privacy concerns because speech carries sensitive information, while offline and open-source systems may transcribe less well than cloud systems in real-world scenarios.The review points to decentralized and federated DTL as privacy-protection directions.Source: §6.2 · p. 25

6.3. Interpretation of DTL models

  • Interpretability remains a challenge for DTL; proposed examples include PCA-based interpretable-embedding distillation and iSTL, whose attention maps highlight clinical features in OCT prediction.The iSTL comparison concerns unseen data and OCT scans, not ASR.Source: §6.3 · pp. 25–26

6.4. Online DTL

  • Online DTL spans homogeneous and heterogeneous feature spaces, with co-occurrence data or transfer with lag addressing some feature-space and data-scarcity challenges.The review also identifies adaptation to changing data and balancing adaptation with retention of prior knowledge as online-learning concerns.Source: §6.4 · p. 26

6.5.1. Using chatGPT as an ASR source model

  • ChatGPT-based transfer is proposed for both acoustic and language modeling, using pretrained knowledge and domain-specific fine-tuning to improve speech recognition.The review describes acoustic feature learning and language representations as potential sources of transferred knowledge.Source: §6.5.1 · p. 26

6.5.2. Using chatGPT for ASR assessment tools

  • ChatGPT is also proposed as an ASR assessment tool, with transcription and MOS outputs compared against ground truth and human ratings.The described evaluation uses WER for ASR and correlation coefficients such as Pearson’s for MOS.Source: §6.5.2 · p. 26

7. Conclusion

The conclusion synthesizes DTL’s breadth across ASR and related applications, reviews unresolved challenges, and identifies future directions. It also notes reported benefits while calling for further work on generalizability and performance.

7. Conclusion

  • The review surveys DTL taxonomies and applications spanning acoustic and language models, cross-domain ASR, emotion recognition, and medical diagnosis.Its examples include inductive, transductive, and adversarial DTL, as well as heart-sound classification and Parkinson’s disease detection.Source: §7 · p. 27
  • The conclusion identifies negative transfer, overfitting, reproducibility, knowledge-gain measurement, and DTL unification as challenges, alongside directions including privacy, interpretation, and online DTL.It also discusses DTL-based LLMs, including ChatGPT as an ASR source model and assessment tool.Source: §7 · p. 27
  • The authors anticipate that DL and DTL research will advance speech technology, particularly ASR and NLP, and may also benefit other fields.The conclusion names medical diagnosis, energy, smart cities, and fault and anomaly detection among those fields.Source: §7 · p. 27
  • DTL has shown less computational cost in some scenarios, less data and label dependence, and better performance in some case studies, but further work is needed on generalizability and overall performance.These are qualified benefits rather than claims that hold in every setting.Source: §7 · p. 27
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