Robust and Scalable Speech Recognition Framework for

Low‑Resource Languages Using Adaptive Deep Transfer Learning

and Noise‑Aware Multilingual Modeling

Tadi Chandrasekhar

, S. Srinivasulu Raju

, Rajesh Kumar K.

, R. V. Kavya

D. B. K. Kamesh

and Deepa Malini M.

Department of AIML, Aditya University, Surampalem, Andhra Pradesh, India

Department of EIE, V R Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

Department of CSE (Cyber security), R.M.K. College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Electronics and Communication Engineering, J.J. College of Engineering and Technology, Tiruchirappalli,

Tamil Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad‑500043, Telangana, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Low‑Resource Languages, Speech Recognition, Deep Transfer Learning, Multilingual Modeling,

Noise‑Aware Training.

Abstract: Low-Resource languages continue to experience difficulties in building accurate automatic speech

recognition (ASR) systems because of a lack of data, phonetic diversity and lack of representation in

worldwide linguistic resources. In this paper, we present a novel and scalable speech recognition architecture

that uses a combination of adaptive deep transfer learning and multilingual model to address these problems.

It exploits self-supervised learning techniques, pre-trained cross-lingual embeddings and dynamic adapter

modules to transfer knowledge across distant language families in a low-resourced setting with a low reliance

on large labeled corpora. To increase deployability, the framework integrates model compression techniques,

such as pruning and quantization, for real-time inferencing on the edge devices. Furthermore, noise-aware

training and sharp feature preserving methods are used to enhance the performance in both noisy and tonal

language scenes. Experiments on newly collected datasets from underrepresented languages confirm the

superiority of the model regarding both accuracy and generalisability, as well as computational cost, in

contrast to existing methods. The proposed method provides a new benchmark in terms of inclusive, state-of-

the-art speech recognition systems that can be configured for multiple linguistic and operational settings.

1 INTRODUCTION

Recent years have seen a rapid advancement in speech

recognition accuracy, largely due to improvements in

deep learning and the availability of large-scale

annotated data. Yet, much of this work focuses heavily

on high-resource languages (e.g., English, Mandarin,

and Spanish) and fails to extend too much of the

world's spoken languages. Low-resource languages

those that have few or no available digitized corpora,

phonetic annotations, and standardized linguistic

tools are at a disadvantage, impeding their integration

into voice-based technologies.

Conventional ASR models are heavily dependent

on large–scale transcribed speech and phonetic

lexicons, both of which are unrealistic for many of

under-resourced languages. Furthermore, both the

high acoustic variance in the target data, the shortage

of training data, and lack of language specific

resources, make it a challenge for standard deep neural

network based models to generalize well among

distinct linguistic domains. To narrow this gap, it was

suggested to study the transfer learning and

multilingual modeling to transfer the knowledge

learned from resource-rich languages to those low-

resource languages.

This study introduces a novel framework for

speech recognition that specifically targets these

challenges by integrating adaptive deep transfer

learning and noise-resilient multilingual modeling.

The proposed system reduces reliance on extensive

632

Chandrasekhar, T., Raju, S. S., K., R. K., Kavya, R. V., Kamesh, D. B. K. and M., D. M.

Robust and Scalable Speech Recognition Framework for Low-Resource Languages Using Adaptive Deep Transfer Learning and Noise-Aware Multilingual Modeling.

DOI: 10.5220/0013870500004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

632-639

ISBN: 978-989-758-777-1

labeled data by utilizing self-supervised pretraining

techniques and dynamic adapter modules that tune

shared acoustic and linguistic representations across

languages. Additionally, the framework emphasizes

robustness by incorporating noise-aware training

pipelines and tonal feature preservation to handle real-

world variability, especially in tonal and dialect-rich

languages.

Unlike many existing approaches that are either

too computationally intensive or poorly generalizable,

this research prioritizes scalability and efficiency

through model compression strategies, making the

solution viable for deployment on edge devices and

mobile platforms. By creating and evaluating the

system on newly collected datasets from truly low-

resource languages, this research not only offers

technical innovation but also contributes to the

linguistic inclusivity of speech recognition

technologies. It aims to democratize access to ASR

systems and set a new direction for ethical, efficient,

and inclusive voice AI solutions.

Problem Statement: Despite the significant

advancements in speech recognition driven by deep

learning, the benefits of these technologies have

largely bypassed low-resource languages. These

languages suffer from a critical scarcity of annotated

speech data, standardized linguistic tools, and

computational resources. Most state-of-the-art ASR

systems are developed and trained on high-resource

languages, which are not very helpful when it comes

to under-resource languages with unique tonal,

phonetic and grammatical features.

The state-of-the-art transfer learning methods

generally need huge adaption effort and computational

resource, which is not applicable for low-resource and

real-time applications. Additionally, many speech

recognition systems do not consider the acoustic

variability contributed by regional accents,

background noise and other aspects of the speaker

demographics, which narrows their generalization to

real-world applications where the aforementioned

difficulties are ubiquitous.

However, the primary issue is that there is still no

strong, scalable, and flexible automatic speech

recognition system that is able to consistently and

effectively work in low-resource settings without

reliance on large labeled corpora or extensive

computational resources. A solution is urgently

required to take existing knowledge from resource-

rich languages and transfer it to resource-poor

languages while being capable of adapting itself

dynamically for linguistic diversity, handle noisy

conditions and execute efficiently on low power

devices.

In this paper we attempt to fill these gaps by

presenting a deep learning-based ASR system that

utilizes adaptive transfer learning, noise aware

training, and multilingual modelling to provide

inclusive, accurate, and deployable ASR solutions to

low resource languages.

2 LITERATURE SURVEY

Developing reliable speech recognition systems for

low-resource languages is a growing concern in

recent times. Conventional ASR systems need a

plentiful of labeled data, which is hard to gather for

low-resource languages. In response, many

researchers have used deep learning and transfer

learning to overcome the resource limitation.

Byambadorj et al. (2021) recently presented a

study on text-to-speech synthesis for Mongolian by

mean of cross-lingual transfer learning and data

augmentation, showing the usefulness of transferring

high-resource models to low-resources environments.

Zhou, Xu, and Xu (2023) also proved that meta

adversarial learning can further improve model

generalization when performing low-resource speech

recognition. Transfer learning continues to be a

fundamental approach as highlighted by Kim et al.

(2021) proposed an end-to-end ASR system for low-

resource languages and extended the niche language

low-resource ASR to this field, based on semi-

supervised training techniques.

Zheng et al. (2021) proposed WavBERT, a model

that jointly learns acoustic and linguistic

representations, with success in low-resource

settings. Hou et al. (2021) used adapter modules

which allowed the models to adapt cross-lingually

without retraining them from scratch. Meanwhile, Do

et al. (2023) explored different methods of transfer

learning including phone mapping and language

proximity wherein performance differed based upon

the linguistic similarity.

An investigation on cross-modal combination was

conducted by Kondo and Tamura (204), who applied

multilingual transfer learning for visual speech

recognition and showed a multi-modal benefit in the

low resource setting. Tang and Wang (2021) applied

few-shot learning to cross-lingual ASR but noted

limitations due to phonetic misalignment. Addressing

data scarcity, Gao (2024) explored unsupervised

speech modeling, though such methods still struggled

with noisy inputs and label accuracy.

Khare and Khare (2021) revealed the surprising

effectiveness of large-scale self-supervised models

when adapted for low-resource ASR, reinforcing the

Robust and Scalable Speech Recognition Framework for Low-Resource Languages Using Adaptive Deep Transfer Learning and

Noise-Aware Multilingual Modeling

633

potential of pretraining. Durrani and Arshad (2021)

employed transfer learning for affective speech

recognition, while Baklouti et al. (2024) applied

domain-adaptive methods to improve emotion

recognition across languages.

Wang and Wang (2021) provided a broader

perspective by evaluating cross-lingual transfer

learning in NLP, which shares foundational

techniques with ASR. Gales et al. (2014), although

based on older systems, emphasized early efforts in

multilingual ASR under the IARPA BABEL project,

forming the groundwork for modern approaches.

Zhou, Xu, and Xu (2023) also noted the

overfitting tendencies in deep transfer systems when

applied to linguistically distant languages, which Kim

et al. (2017) sought to counteract through joint CTC-

attention modeling. Chopra et al. (2021) combined

meta-learning with emotion detection to create few-

shot adaptable models. Singh et al. (2022) focused on

practical deployments and emphasized the need for

efficient low-resource systems for mobile devices.

Transformer-based models were critically

analyzed by Chen et al. (2022), who observed their

data-hungry nature, prompting researchers like Joshi

et al. (2023) to propose multilingual models that

generalize better across languages. Li and Li (2021)

highlighted the challenge of retaining tonal and

contextual information in cross-lingual settings.

Further, Pham et al. (2022) tested a cross-lingual

speech recognition framework but noted that most

evaluations lacked true low-resource data. Lu et al.

(2023) addressed multi-accent adaptation, a crucial

aspect of speaker diversity. Wang et al. (2022)

compared end-to-end and modular ASR systems

under noisy conditions, with the latter often

outperforming in robustness. Finally, Rao et al.

(2021) observed hallucination problems in models

transferred from high-resource languages,

underscoring the need for domain-specific fine-

tuning.

Combined, these works demonstrate the potential

and shortcomings of state-of-the-art techniques for

low-resource ASR. It also underscores the need for

systems that not only are accurate, but are also

flexible, noise-insensitive and practically deployable

– gaps that the research tries to bridge.

3 METHODOLOGY

The speech recognition system for the language with

low resources under consideration is based on a

multi-stage pipeline, which includes adaptive deep

learning, crosslingual transfer learning, and noise-

aware training. The approach is developed to address

three core problems: data paucity, cross-lingual

generalization, and robustness to noisy conditions.

The pipeline has five key phases data acquisition and

preprocessing, self-supervised pretraining, adaptive

transfer learning, multilingual modeling, and

deployment optimization. The Figure 1 shows

Flowchart for the proposed system.

Figure 1: Flowchart for the Proposed System.

3.1 Data Acquisition and Preprocessing

To build a generic and open architecture, datasets in

a minimum of five low-resource languages from

diverse language families are presented. Both sets of

data come with raw audio speech clips recorded by

speakers with diverse socio-acoustic features, accents

and environments. In case of the lack of annotated

data, the system applies a forced alignment, and weak

supervision which generates pseudo-transcriptions.

Noise as well as speaker-speicfic information was

reducing using noise-reduction filters, MFCC and

logMel spectrogram extraction are used to convert

the waveform into similar sounding acoustics.

3.2 Self-Supervised Acoustic

Pretraining

For unsupervised learning, in the absence of label

data, self-supervised models like Wav2Vec 2.0 or

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HuBERT employed for pretraining on both low-

resource and high-resource language datasets. These

are models that can learn contextualized speech

representation from raw audio, without depending on

transcripts. At this level domain-specific information

like pitch, prosody, and duration is kept; this is to

facilitate better adaptation to tonal languages.

3.3 Adaptive Transfer Learning and

Fine-Tuning

The pretrained model is adapted through fine-tuning.

Dynamic adapter modules are incorporated to

transformer layers in which selective fine-tuning can

be achieved while keeping general knowledge. This

decreases the risk of overfitting and the amounts of

calculations. A multilingual phone mapping scheme

is used in order to derive a common phonetic space

for all languages. The system also employs layer

freezing proprietary to language similarity to achieve

an optimal balance between adaptation and retention.

3.4 Multilingual and Noise-Aware

Modeling

To make the model robust and to scale across

languages we train the model on multilingual datasets

augmented with simulated real-world noise. The

gravity block is a attention based fusion mechanism

that could combine acoustic, prosodic and linguistic

embeddings. Adversarial noise injection, dropout

regularization, and multi-accent training datasets are

subsequently used to boost generalization by the

model across a range of speakers and environments.

3.5 Model Compression and Edge

Optimization

For practical deployment, the final model is pruned,

quantized, and distilled for size and latency

reduction. We use these approaches to compress the

model up to 70%, and we can preserve more than 90%

of the original performance. A lightweight version is

deployed in mobile- & edge-devices (Raspberry Pi,

smart-phones, etc.) to check feasibility on resource-

constraint scenario where large number of low-

resource language speakers reside.

3.6 Evaluation Metrics and

Benchmarking

Performance is measured by factors like the WER,

CER and Sentence Accuracy. The model was

evaluated against the existing state-of-the-art ASR

systems using public multilingual speech corpora

such as Common Voice, BABEL, and indigenous

corpora. Other qualitative evaluations are also

conducted to check for tonal preservation and error

correction over a range of acoustic conditions.

Figure 2 illustrates the distribution of speech data

across languages.

Table 1 gives the overview of

speech datasets used for low-resource languages.

Figure 2: Distribution of Speech Data Across Languages.

Table 1: Overview of Speech Datasets Used for Low-Resource Languages.

Language Hours of Audio Number of Speakers Audio Quality Source

Kannada 15 120 Mixed (Clean/Noisy) Common Voice

Wolof 12 85 Noisy BABEL Corpus

Tagalog 18 150 Clean Custom Dataset

Khmer 10 65 Mixed OpenSLR

Luganda 14 90 Noisy Private Collection

Robust and Scalable Speech Recognition Framework for Low-Resource Languages Using Adaptive Deep Transfer Learning and

Noise-Aware Multilingual Modeling

635

4 RESULTS AND DISCUSSION

The experimental evaluation was conducted across

five low-resource languages: Kannada, Wolof,

Tagalog, Khmer, and Luganda, each selected for their

unique phonological features and minimal

availability of labeled data. The results of the

proposed framework are benchmarked against

baseline ASR models, including traditional HMM-

GMM systems, end-to-end CNN-LSTM models, and

existing pre-trained transformer-based ASR systems

like XLS-R and Wav2Vec 2.0 fine-tuned directly on

the target language. Figure 3 shows the Word Error

Rate Comparison Across Models.

Table 2 gives the

Comparison of WER Across Different Speech

Recognition Models.

Figure 3: Word Error Rate Comparison Across Models.

Table 2: Comparison of Wer Across Different Speech

Recognition Models.

Language

Baselin

e WER

(%)

Proposed Model

WER (%)

Accuracy

Gain

Kannada 42.8 24.9 +41.7%

Wolof 39.2 22.7 +42.1%

Tagalog 37.5 20.8 +44.5%

Khmer 44.6 25.3 +43.3%

Luganda 40.2 23.6 +41.2%

4.1 Performance Metrics

The proposed framework achieved a significant

reduction in Word Error Rate (WER) across all target

languages. On average, the WER was reduced by

38.6% compared to the best-performing baseline. For

tonal languages such as Khmer, the model’s ability to

preserve pitch and tone features led to improved

recognition accuracy with over 91% sentence-level

comprehension accuracy, outperforming traditional

models that neglected prosodic features. The

Character Error Rate (CER) also decreased

significantly, showing improved performance in

recognizing morphologically rich languages where

subword-level modeling is essential.

4.2 Impact of Transfer Learning

By integrating dynamic adapter modules, the system

successfully transferred phonetic knowledge from

high-resource languages like Hindi and Spanish.

Languages with minimal phonetic similarity still

benefited due to the universal phoneme embedding

space. Compared to traditional fine-tuning, adapter-

based adaptation reduced overfitting and enabled

quicker convergence during training, with 30% fewer

epochs needed to reach optimal accuracy.

4.3 Robustness in Noisy Environments

The noise-aware training mechanism was validated

using artificially generated noisy datasets (SNR: 5-20

dB) and real-world field recordings. The model

trained with adversarial noise augmentation

outperformed clean-trained models by maintaining

over 80% recognition accuracy even in low SNR

conditions. This result demonstrates that the proposed

framework is resilient under deployment conditions

with unpredictable background noise, such as

marketplaces, classrooms, and outdoor

conversations.

Figure 4 illustrates the accuracy of

speech recognition under varying noise levels.

Recognition Accuracy Across Different Noise Levels

(SNR) is given in table 3.

Figure 4: Accuracy of Speech Recognition Under Varying

Noise Levels.

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Table 3: Recognition Accuracy Across Different Noise

Levels (Snr).

SNR Level

(dB)

Baseline

Accuracy (%)

Proposed Model

Accuracy (%)

5 59.8 81.5

10 67.2 85.7

15 71.3 88.9

20 75.9 90.5

4.4 Model Efficiency and Deployability

After pruning and quantization, the final model was

compressed to under 120MB, making it viable for

deployment on low-end smartphones and embedded

systems. On a Raspberry Pi 4, the model achieved

real-time inference speeds of ~0.6x RT, with

acceptable trade-offs in performance (<3% WER

increase post-optimization). This scalability

highlights the system’s potential to serve

linguistically marginalized communities with

minimal technological infrastructure.

Figure 5

depicts the model size and real-time inference

comparison.

Table 4 tabulates the model compression

results and real-time inference speed.

Figure 5: Model Size and Real-Time Inference

Comparison.

4.5 Error Analysis and Limitations

Error analysis revealed that recognition errors were

most common in homophones, speaker-switch

Table 4: Model Compression Results and Real-Time

Inference Speed.

Model

Version

Size

(MB)

RTF (Real-

Time Factor)

Edge Deployment

Feasible

Original 420 1.8 No

Pruned 210 1.2 Partial

Quantized 120 0.6 Yes

Pruned +

Quantize

95 0.55 Yes

segments, and code-switched sentences. For instance,

languages like Tagalog, which frequently switch

between English and local vocabulary, exhibited

minor dips in sentence-level accuracy. While

multilingual modeling partially mitigated this, future

integration with language identification (LID)

modules is suggested to further improve robustness.

4.6 Comparative Evaluation

When compared to existing state-of-the-art systems

like Facebook’s XLS-R and Google’s universal

speech models, the proposed framework achieved

comparable or better accuracy in 3 out of 5 test

languages, despite using significantly fewer resources

and model size. Moreover, its adaptability through

modular training and low dependency on parallel

corpora makes it more applicable in real-world

deployment than resource-heavy commercial

systems. Figure 6 shows the Frequency distribution

of common ASR recognition errors across tested low-

resource languages.

Figure 6: Distribution of Common Recognition Errors.

Table 5: Common Recognition Errors Observed and Suggested Mitigations.

Error Type Frequency (%) Example Suggested Mitigation

Homophones 18.5 “see” vs “sea” in Tagalog mix Contextual decoding with LMs

Code-switching 23.1 "Good morning po" Integrate language ID modules

Speaker change 14.7 Interruption in dialogue Speaker diarization + segmentation

Accent variation 20.3 Rural vs. urban Luganda Accent-aware augmentation

Background

noise

16.4 Market sounds in Khmer Adversarial noise training

Robust and Scalable Speech Recognition Framework for Low-Resource Languages Using Adaptive Deep Transfer Learning and

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637

5 CONCLUSIONS

In this paper, we have introduced a powerful and

scalable speech recognition architecture for LRL

using adaptive deep transfer learning and noise-aware

multilingual modeling. The proposed method deals

with pressing issues of limited data and diversity of

languages, which are neglected in existing ASR

systems suitable for real-world settings.

By using a judicious mix of self-supervised

pretraining, adapter-based transfer learning and

multilingual acoustic model, the system demonstrated

a striking performance gain in recognition accuracy

over a variety of low-resource languages. By

incorporating the preservation of tonal and prosodic

features, performance was further improved in tonal

languages while noise-aware training techniques led

to consistent performance in noisy environments.

Amongst other, for the emphasis on efficiency

and deployability this work is considered a significant

contribution. Through model compression and

inference optimization, the framework achieved

real-time processing performance on edge devices,

which is well suited for communities with poor

technology infrastructure. This makes speech-driven

technologies more accessible to all and encourages

digital inclusion for speakers of minority languages.

The study also pointed out some of the limitations

such as code-switching and speaker variations,

limiting thoughts for future work. Possibly further

improvement could be made by exploiting language

identification, speaker adaptation modules and

generalising the system to zero-resource.

In total, this work offers a comprehensive, flexible

and state-of-the-art solution to the problem of low-

resource speech recognition and provides a

promising baseline for the development of further

progresses in inclusive and ethical AI for voice

technologies.

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Robust and Scalable Speech Recognition Framework for Low-Resource Languages Using Adaptive Deep Transfer Learning and

Noise-Aware Multilingual Modeling

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