English Pronunciation Scoring Model

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@letsUnlockAI

Hi, I’m an ML engineer with hands-on experience building speech and pronunciation-scoring systems for language-learning apps. I can own the full pipeline—from selecting a robust acoustic/phoneme-alignment backbone (wav2vec2 / HuBERT / Whisper), through training and evaluation, to exporting a low-latency, inference-ready model. You’ll get word- and sentence-level scores, a clear quality label, Dockerized deployment, and a concise report aligned with real user usage.

@amirrmusavifr

Hi, I'm an experienced Python developer with the necessary skills to complete your project. I already worked on NLP project topics including skill sets: • Proficiency in Machine Learning techniques, deep networks (CNN, RNN, LSTM, GRU, Attention Mechanism), • Experience with Chatbot, Question Answering, Sentiment Classification, Named Entity Recognition (NER), Part of Speech (POS) tagging, Lemmatization, Text Similarity, Machine Translation etc. • Fine-tune ChatGPT, GPT (2, 3, 3.5turbo), LLM, BERT, Gemini, Llama, … based on the specific requirements and functionalities. • Experience training or adjusting LLMs (Hugging Face, DeepSpeed). • Strong programming skills, preferably in Python and relevant libraries like Pytorch, TensorFlow, scikit-learn, NumPy, Pandas, NLTK, spaCy, etc. which my skillset allows me to handle large datasets I believe I am the perfect fit for this project. With my skill set, you can be sure that you will receive high-quality results. If you're interested in hearing more about how I could help you, please don't hesitate to reach out! I can provide the requirements with minimum time and cost.

@Rahulsingh5xz

Hello, I’m Rahul Singh from Team Velora, and for the past 3 years we’ve built production-ready ML pipelines for speech and pronunciation assessment, optimized for real-time scoring in mobile apps. I can deliver a model that returns word-level and sentence-level accuracy along with a “good/poor” quality label, complete with inference API, deployment instructions, and performance optimized for scalability and low latency. Please come in private chat so I can share relevant pronunciation-scoring and speech-assessment projects and discuss dataset access and workflow.

@devanshkhandekar

I can help you in developing a production-ready pronunciation scoring model . As a former Senior ML Engineer, I specialize in speech AI, including ASR, TTS, and real-time inference. I have deep expertise in Wav2Vec2, Whisper, and Kaldi, specifically implementing GOP (Goodness of Pronunciation) and PER (Phoneme Error Rate) engines. I can deliver a full ML engine, API, and documentation in 4 days. Technical Capabilities: Multilingual Support: Proven pipelines for English, Cantonese, Arabic, French, and Vietnamese. Optimization: Lightweight models for mobile or CPU-based server deployment. Precision: Fine-tuned phoneme alignment and error-preserving transcription. Architecture Options: Kaldi-based (high reliability), Wav2Vec2 (multilingual flexibility), or Hybrid PER/GOP (maximum granularity). Deliverables: Your system will generate structured API responses featuring word/sentence-level accuracy and "Good/Poor" quality labels. You will receive scalable, modular code with complete test cases and documentation. I am currently offering senior-level expertise at competitive freelance rates to establish new partnerships. The bid provided is a placeholder; I am available for a call to finalize the technical scope, accuracy benchmarks, and pricing.

@hr157

Hello, I’d be excited to own and deliver the full pronunciation-scoring ML pipeline for your language-learning app—from acoustic modeling through inference-ready deployment. I’ve worked on speech and ML systems where latency, scalability, and interpretability are critical, and I’m comfortable taking models from research to production. How I’ll approach this: • Select a strong backbone (e.g., wav2vec 2.0 / HuBERT / Whisper) based on your latency and accuracy goals, with forced alignment for word-level and sentence-level scoring. • Train a pronunciation scoring layer to output continuous accuracy scores plus a simple good/poor label, tuned for Grades 6–9 English learners and both isolated words and connected speech. • Evaluate against human-rated benchmarks (correlation, RMSE, classification accuracy) and document threshold tuning recommendations. • Export an inference-ready artifact (PyTorch / ONNX) with clean, JSON-based APIs for real-time or batched scoring. • Optimize for scale—considering batching, streaming inference, and optional lightweight/on-device paths. Deliverables you’ll receive: • Trained model + reproducible training scripts and hyper-parameters • Inference service returning word scores, sentence score, and quality label • Clear evaluation report (data, metrics, limitations) • Dockerized deployment instructions ready for backend integration I’m comfortable aligning early on dataset access, target correlation with human raters, and milestone planning.

@nexvative

I can design and deliver a production-ready spoken-English pronunciation assessment system that provides real-time, interpretable feedback suitable for mobile app integration. My focus will be low latency, scoring transparency, and a clean inference architecture that scales reliably in production. Proposed approach • Audio ingestion & preprocessing: real-time microphone input, noise handling, VAD, and feature extraction optimized for mobile latency. • Speech–text alignment: forced alignment between learner audio and expected text to support both isolated words and connected speech. • Pronunciation scoring: phoneme- and word-level accuracy metrics aggregated into sentence-level scores, plus a clear Good / Poor quality label following your scoring logic. • Real-time feedback: streaming or near-real-time inference so learners receive immediate guidance. • Model stack: proven ASR / pronunciation-scoring models (e.g., wav2vec-style encoders or hybrid ASR + alignment pipelines), tuned for inference speed and consistency. Deliverables • Pronunciation scoring service implementing the full flow in your spec • Documented API for mobile integration • Clear notes on latency, scaling, and model update strategy I’ve built speech and ML inference systems where accuracy, explainability, and response time are all critical. If you’d like, I can walk through a similar real-time ML pipeline I’ve delivered and discuss how I’d align this build precisely with your attached requirements.

@dushyant317

I bring 13 years of professional experience delivering high-quality results. I have strong expertise in all the required skills listed for this project. My approach ensures accuracy, clear communication, and timely delivery. I am confident I can exceed your expectations with efficient, reliable work. Looking forward to contributing to your project—ready to begin immediately.

@SebastianVDM

Good day, I’m excited to apply for the English Pronunciation Scoring Model project for your mobile language-learning app. This sounds like a fantastic opportunity to develop a tool that enhances language acquisition through pronunciation feedback. I understand that you need a production-ready machine learning model capable of assessing and scoring pronunciation. With my background in Python, Machine Learning, and Natural Language Processing, I can create a robust model that analyzes audio input and provides accurate feedback. My approach will involve utilizing relevant datasets to train the model and ensure it meets your app's requirements for user-friendliness and efficiency. While I’m new to Freelancer, I bring a wealth of experience from numerous off-platform projects. I am looking forward to discussing your vision further and exploring how we can collaborate to make your app a success. Please feel free to reach out with any questions or additional details. Best Regards, Sebastian.

@MuhammadAsad108

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Data Review & Target Definition ⟶⟶ Acoustic Model Selection (wav2vec / HuBERT / Whisper) ⟶⟶ Pronunciation Scoring Architecture Design ⟶⟶ Model Training & Evaluation ⟶⟶ Word & Sentence Scoring Logic ⟶⟶ API & Inference Layer ⟶⟶ Dockerised Deployment & Handoff Key Highlights ✔ End-to-end ML ownership — from model selection and training to deployment-ready inference. ✔ Pronunciation scoring — numeric accuracy scores at both word-level and sentence-level. ✔ Quality classification — simple, interpretable “good / poor” pronunciation label. ✔ Supports isolated words & connected speech — suitable for drills and free-speaking exercises. ✔ State-of-the-art backbone — wav2vec 2.0 / HuBERT / Whisper (chosen based on latency & accuracy trade-offs). ✔ Low-latency inference — batching/streaming-friendly design suitable for thousands of concurrent users. ✔ Clean API — JSON input/output returning scores, labels, and alignment details. ✔ Production-ready delivery — Dockerised setup with clear deployment instructions. ✔ Extensible architecture — ready to expand to Spanish, Mandarin, or additional grade levels. Best Regards, Asad Machine Learning Engineer | Speech & NLP Systems | Production AI Deployment

@jennierubyjanew

Hello, I am an experienced machine learning engineer with expertise in speech processing, acoustic modeling, and real-time inference. I can deliver a production-ready pronunciation-scoring model for your language-learning app that meets your requirements for word-level and sentence-level accuracy scoring, as well as a simple “good/poor” quality label. What I can provide: Full ownership of the ML pipeline: data preprocessing, model selection (wav2vec, HuBERT, DeepSpeech, Whisper, or custom PyTorch/TensorFlow solutions), training, evaluation, and deployment. Inference-ready artifact optimized for low latency and scalable usage, with options for batching, streaming, or lightweight on-device inference. Clean API (JSON in/out) returning word-level scores, sentence-level score, and categorical labels. Documentation of hyperparameters, training scripts, and evaluation methodology. Dockerized deployment instructions for seamless integration into your backend. Approach highlights: Support both isolated words and connected speech, tailored for Grades 6–9 English learners. Evaluation against human rater benchmarks and recommended threshold tuning. Extensible architecture for future languages (Spanish, Mandarin). I have prior experience with speech recognition and pronunciation scoring systems and can start immediately to meet your timeline. I’d love to jump on a quick call to discuss dataset access, correlation targets, and milestones. Best regards, Zarnigor

@asad2026

Hello, I can deliver a production-ready pronunciation scoring model for your English language-learning app, covering both isolated words and connected speech. The system will return word-level scores, a sentence-level score, and a clear Good/Poor label, designed for low-latency, scalable use. I’ll own the full ML pipeline: selecting a strong speech backbone (e.g., wav2vec/HuBERT or similar), training and evaluation, and exporting an inference-ready model (PyTorch/ONNX). The solution will include a clean JSON API, documented training scripts, and dockerised deployment for easy backend integration. You’ll also receive a short evaluation report outlining data usage, metrics, and threshold-tuning guidance. The architecture will be modular so it can later extend to Spanish or Mandarin. Happy to discuss dataset access, target accuracy vs. human raters, and timeline on a quick call.

@jaroslavcaprata

Hi, there. I will build a production-ready English pronunciation scoring system using Python, deep learning speech models, and NLP, handling audio-to-text alignment, phoneme comparison, real-time scoring at word and sentence level, and deploying it as a low-latency inference API suitable for mobile integration. My focus is on accuracy, speed, and scalability so the system delivers clear feedback learners can trust. I have built a similar speech evaluation model for an edtech app with 50,000+ users, achieving under 1.5s average response time and improving pronunciation accuracy scores by 38% after deployment. I design ML systems that move cleanly from research to real-world use without surprises. You can be confident I can take this from spec to production, follow the provided architecture closely, and anticipate scaling and mobile integration needs so the model performs reliably as usage grows. Thanks.

@Chanda9876

I can build a production-ready, low-latency spoken English evaluation system that delivers real-time pronunciation feedback at word and sentence level, designed specifically for mobile app integration and scalable inference deployment. I have experience delivering speech-based ML systems end-to-end, including audio ingestion, alignment against reference text, pronunciation scoring, and API-based inference services. I’m comfortable translating detailed functional specs (scoring logic, flow, and architecture) into a clean, maintainable production system.

@karthikresonite

Resonite Technologies offers to build your production-ready English pronunciation scoring system with real-time feedback and low-latency performance suitable for mobile integration. Our team specializes in speech processing and ML deployment, enabling us to deliver a scalable inference service that evaluates microphone input against expected text. Proposed solution: • Real-time audio capture and preprocessing for isolated words and connected speech • ML-driven scoring model producing word- and sentence-level accuracy with a quality label (good/poor) • Low-latency inference suitable for mobile apps, using optimized models (PyTorch/TensorFlow Lite/ONNX) • Scalable deployment via REST/GRPC API for integration into Android/iOS apps • Logging and analytics for pronunciation trends and model improvement Deliverables: • Fully functional scoring system aligned with your specifications • Documentation for API endpoints, model usage, and integration • Tested inference service with sample mobile app integration • Optional future support for language expansion or improved scoring logic We will carefully review your functional requirements document and implement the scoring flow, evaluation logic, and deployment architecture exactly as specified. Best regards Resonite Technologies