BestHub
Sign in
Tagged articles
23 articles
Page 1 of 1
Weekly Large Model Application
Weekly Large Model Application
May 5, 2026 · Artificial Intelligence

Where Is End‑to‑End Speech AI Heading? Product vs Engineering Perspectives

The article clarifies the dual meaning of “end‑to‑end” in speech AI—product simplicity and engineering unification—then outlines six emerging trends, from real‑time conversational latency to multilingual robustness, token‑based audio pipelines, voice‑specific security, edge privacy, and the growing importance of data quality and reproducibility.

Edge ComputingEnd-to-EndSpeech AI
0 likes · 8 min read
Where Is End‑to‑End Speech AI Heading? Product vs Engineering Perspectives
Woodpecker Software Testing
Woodpecker Software Testing
Apr 19, 2026 · Artificial Intelligence

Deep Dive into AI Agent Testing: From LLMs to Autonomous Agents

The article analyzes why testing AI agents differs from LLM testing, outlines four major testing challenges, and presents a four‑layer TAME validation framework with real‑world examples, while forecasting emerging trends such as test‑as‑code and industry‑wide benchmarks.

AI AgentAction SequenceEnd-to-End
0 likes · 8 min read
Deep Dive into AI Agent Testing: From LLMs to Autonomous Agents
Weekly Large Model Application
Weekly Large Model Application
Mar 13, 2026 · Artificial Intelligence

Speech Large Models: Why End-to-End Architecture Beats Traditional ASR‑LLM‑TTS Pipelines

The article defines true speech large models as native end‑to‑end systems that directly map audio to audio, compares them with traditional cascade ASR‑LLM‑TTS pipelines across architecture, error control, latency, paralinguistic perception, long‑context handling and deployment, and surveys the leading open‑source and commercial speech LLMs released in March 2026 with a quick selection guide.

AIASREnd-to-End
0 likes · 11 min read
Speech Large Models: Why End-to-End Architecture Beats Traditional ASR‑LLM‑TTS Pipelines
Old Zhao – Management Systems Only
Old Zhao – Management Systems Only
Jun 10, 2025 · Operations

Why Digital Procurement Fails and How to Build End‑to‑End Collaboration

This article explains why many digital procurement projects fail, identifies five common pitfalls—from neglecting process redesign to siloed systems—and offers a step‑by‑step end‑to‑end collaboration framework that aligns demand, approval, planning, supplier, and data flows for true procurement digitalization.

CollaborationDigital TransformationEnd-to-End
0 likes · 9 min read
Why Digital Procurement Fails and How to Build End‑to‑End Collaboration
Baidu Tech Salon
Baidu Tech Salon
Jan 8, 2025 · Artificial Intelligence

Evolution of Video Search Ranking Architecture Toward an End‑to‑End Large‑Model Framework

The paper describes transforming a tightly coupled, multi‑stage video search ranking pipeline into a modular, end‑to‑end large‑model architecture that decouples recall, employs a graph‑engine parallel framework and elastic compute allocation, thereby boosting performance, flexibility, personalization and lowering long‑term operational costs.

End-to-EndSystem optimizationelastic resources
0 likes · 10 min read
Evolution of Video Search Ranking Architecture Toward an End‑to‑End Large‑Model Framework
ZhongAn Tech Team
ZhongAn Tech Team
Dec 8, 2024 · Artificial Intelligence

Weekly AI Digest Issue 5: Voice Interaction Trends, End‑to‑End vs. Chain Integration, and Enterprise Solutions

This issue examines the growing importance of voice interaction in AI, highlights Justin Uberti’s move to OpenAI and the launch of GPT‑4o, compares end‑to‑end large‑model and chain‑integration approaches, and offers practical enterprise deployment scenarios for both weak and strong voice‑based interactions.

AIChain IntegrationEnd-to-End
0 likes · 14 min read
Weekly AI Digest Issue 5: Voice Interaction Trends, End‑to‑End vs. Chain Integration, and Enterprise Solutions
Ops Development & AI Practice
Ops Development & AI Practice
Jun 17, 2024 · Fundamentals

End‑to‑End vs Divide‑and‑Conquer: Which Algorithm Wins in Real‑World Scenarios?

This article compares end‑to‑end and divide‑and‑conquer algorithmic approaches, outlining their definitions, strengths, weaknesses, and ideal use‑cases, and illustrates the differences with concrete examples in image classification and sorting, helping developers choose the most suitable method for performance and reliability.

End-to-EndSortingalgorithm
0 likes · 7 min read
End‑to‑End vs Divide‑and‑Conquer: Which Algorithm Wins in Real‑World Scenarios?
Continuous Delivery 2.0
Continuous Delivery 2.0
Aug 22, 2023 · Fundamentals

Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops

The article examines why end‑to‑end testing strategies frequently break in practice, illustrates common pitfalls through a realistic scenario, and proposes a more reliable testing pyramid that emphasizes fast, reliable, isolated unit and integration tests while establishing quick feedback loops for developers.

End-to-EndFeedback LoopTest Strategy
0 likes · 10 min read
Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops
Bilibili Tech
Bilibili Tech
Feb 28, 2023 · Artificial Intelligence

High‑Quality Automatic Speech Recognition (ASR) Solutions at Bilibili: Data, Model, and Deployment Optimizations

Bilibili’s high‑quality ASR system combines large‑scale filtered business data, semi‑supervised Noisy‑Student training, an end‑to‑end CTC model with lattice‑free MMI decoding, and FP16‑optimized FasterTransformer inference on Triton, delivering top‑ranked accuracy, low latency, and scalable deployment for diverse Chinese‑English video content.

ASRBilibiliEnd-to-End
0 likes · 18 min read
High‑Quality Automatic Speech Recognition (ASR) Solutions at Bilibili: Data, Model, and Deployment Optimizations
DataFunSummit
DataFunSummit
Oct 20, 2022 · Artificial Intelligence

End-to-End Speech Relation Extraction

This paper presents an end‑to‑end approach for extracting relational triples directly from speech signals, bypassing intermediate transcription, and demonstrates its effectiveness on synthesized speech versions of the CoNLL04 and TACRED datasets, highlighting challenges such as length constraints and cross‑modal alignment.

End-to-Endmultimodalnatural language processing
0 likes · 17 min read
End-to-End Speech Relation Extraction
Architects' Tech Alliance
Architects' Tech Alliance
Oct 3, 2022 · Artificial Intelligence

DPU Performance Benchmark Methodology and Implementation (2022)

This article details the DPU performance benchmark testing framework, describing three test system architectures—Single-End, End-to-End, and Multi-End—along with their hardware and software components, test workflow, result reporting requirements, and the necessary tools and drivers for accurate and repeatable performance evaluation.

DPUEnd-to-EndMulti-End
0 likes · 9 min read
DPU Performance Benchmark Methodology and Implementation (2022)
58 Tech
58 Tech
Sep 29, 2022 · Artificial Intelligence

End-to-End Speech Recognition Optimization and Deployment at 58.com

58.com’s AI Lab presents a comprehensive overview of its end‑to‑end speech recognition system, detailing data collection, semi‑supervised training, Efficient Conformer architecture, model compression, and deployment strategies that together achieve high accuracy across diverse acoustic conditions and large‑scale production workloads.

AIDeploymentEfficient Conformer
0 likes · 19 min read
End-to-End Speech Recognition Optimization and Deployment at 58.com
DataFunTalk
DataFunTalk
Jul 7, 2022 · Artificial Intelligence

Huawei Translation’s Achievements and Technical Solutions in IWSLT 2022 Speech Translation Tasks

This article reviews Huawei Translation’s top-ranking results in the IWSLT 2022 speech translation competition across speech‑to‑speech, offline speech‑to‑text, and length‑controlled translation tasks, and details their cascade and end‑to‑end technical approaches, including domain‑controlled ASR, context‑aware MT re‑ranking, and VITS‑based TTS.

ASREnd-to-EndHuawei
0 likes · 13 min read
Huawei Translation’s Achievements and Technical Solutions in IWSLT 2022 Speech Translation Tasks
Continuous Delivery 2.0
Continuous Delivery 2.0
Apr 5, 2022 · Fundamentals

Just Say No to More End-to-End Tests

While end-to-end tests seem appealing by mimicking real user scenarios, they often become unreliable, slow, and costly, leading teams to miss bugs, waste time, and struggle with feedback loops; a balanced testing strategy that emphasizes fast, reliable unit and integration tests, following the test pyramid, yields better quality and faster releases.

End-to-EndSoftware qualityintegration testing
0 likes · 14 min read
Just Say No to More End-to-End Tests
DataFunSummit
DataFunSummit
Nov 18, 2021 · Artificial Intelligence

Enterprise Applications and Research of Speech Translation

This article reviews recent advances in speech translation, discusses ByteDance's practical deployments, compares cascade and end‑to‑end modeling approaches, introduces improved encoder‑decoder architectures and training strategies, and reports state‑of‑the‑art results on the IWSLT 2021 benchmark.

AIByteDanceEnd-to-End
0 likes · 15 min read
Enterprise Applications and Research of Speech Translation
58 Tech
58 Tech
Aug 19, 2020 · Artificial Intelligence

Speech Recognition in 58.com: Application Scenarios, Data Collection, Kaldi Chain Model Practice, and End‑to‑End Exploration

This article presents a comprehensive overview of how 58.com leverages large‑scale voice data from call‑center, private phone, and micro‑chat platforms, detailing data collection, annotation, Kaldi‑based chain model training, lattice‑free techniques, and end‑to‑end Transformer‑CTC models to improve Chinese speech recognition performance.

ASRChineseDeep Learning
0 likes · 16 min read
Speech Recognition in 58.com: Application Scenarios, Data Collection, Kaldi Chain Model Practice, and End‑to‑End Exploration
FunTester
FunTester
Mar 16, 2020 · Fundamentals

Common Reasons Why End-to-End Automation Testing Fails and How to Avoid Them

The article outlines why end-to-end test automation often fails—such as hiring the wrong people, neglecting code quality, underestimating long-term effort, misunderstanding automation scope, limited test coverage, and poor visibility—and offers practical guidance to improve automation success.

End-to-EndSoftware qualityautomation testing
0 likes · 11 min read
Common Reasons Why End-to-End Automation Testing Fails and How to Avoid Them
DataFunTalk
DataFunTalk
Feb 13, 2020 · Artificial Intelligence

Deep Learning Techniques and Challenges in Autonomous Driving

This article reviews the rapid development of deep learning, its pivotal role in autonomous driving, outlines end‑to‑end perception‑to‑control pipelines, discusses the strengths and limitations of deep models, and proposes practical strategies such as task decomposition, multi‑method fusion, and sensor integration to improve safety and interpretability.

Computer VisionDeep LearningEnd-to-End
0 likes · 8 min read
Deep Learning Techniques and Challenges in Autonomous Driving
WecTeam
WecTeam
Jan 7, 2020 · Frontend Development

Master Vue Testing: From Unit to End-to-End with Mocha, Cypress, and Vue CLI

Learn how to write comprehensive tests for Vue applications—including unit, integration, and end-to-end tests—using Vue CLI, Mocha, Chai, Vue Test Utils, Vuex, Vue Router, Sinon, and Cypress, with practical code examples that demonstrate test setup, mocking, and browser verification.

CypressEnd-to-EndIntegration Tests
0 likes · 17 min read
Master Vue Testing: From Unit to End-to-End with Mocha, Cypress, and Vue CLI
Hulu Beijing
Hulu Beijing
Apr 25, 2019 · Artificial Intelligence

How to Build End-to-End Deep Learning Models for Self-Driving Cars

This article reviews the evolution of autonomous‑driving research, explains how to design end‑to‑end deep‑neural‑network models such as PilotNet, and outlines a reinforcement‑learning based decision system, highlighting key architectures, performance metrics, and future challenges.

Deep LearningEnd-to-EndPilotNet
0 likes · 9 min read
How to Build End-to-End Deep Learning Models for Self-Driving Cars
JD Tech
JD Tech
Dec 6, 2018 · Operations

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

JD's chief scientist Shen Zuo‑jun explains how shortening the decision chain with end‑to‑end algorithms and intelligent multi‑level replenishment dramatically improves inventory turnover, stock availability, and forecasting accuracy, showcasing a novel supply‑chain research direction that integrates AI, big data, and human expertise.

End-to-EndOperationsforecasting
0 likes · 9 min read
Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain
Didi Tech
Didi Tech
Jun 1, 2018 · Artificial Intelligence

Didi's Attention-Based End-to-End Mandarin Speech Recognition: A Detailed Review

Didi’s attention‑based end‑to‑end Mandarin speech recognizer, built on the Listen‑Attend‑Spell architecture and modeling roughly 5,000 common characters, delivers 15‑25% relative accuracy gains over its prior LSTM‑CTC system while cutting model size, latency and server requirements and simplifying training by eliminating separate acoustic, pronunciation and language components.

End-to-EndLASMandarin
0 likes · 14 min read
Didi's Attention-Based End-to-End Mandarin Speech Recognition: A Detailed Review