Baidu AI Cloud Media and Entertainment China Translated from Chinese

CCTV News (央视新闻)

CCTV News needed a more accessible way to deliver live and recorded media to hearing-impaired audiences, because human sign-language resources are scarce and conventional media experiences leave many people unable to follow fast-moving broadcast content.

ASR accuracy exceeded 98%
broadcast more than 200 news items
mouth-shape generation accuracy exceeded 98.5%
served 27 million hearing-impaired users

Reported impact

Business outcome

CCTV News expanded media accessibility for hearing-impaired audiences, proved that sign-language digital humans can work at broadcast scale, and created a reusable platform for broader accessible public-service scenarios.

  • Customer Experience
  • Scalability

Solution

Baidu AI Cloud Xiling and CCTV News built an AI sign-language anchor that translates mainstream media content into synchronized sign-language performance for hearing-impaired audiences. The solution breaks the problem into three layers: speech must first be recognized accurately, Chinese text must then be translated into sign-language code with the right grammar and keyword structure, and finally a digital human must express the result through coordinated hand movement, mouth shape, and body language. Baidu's speech-recognition model provides the first layer, while a sign-language translation workflow was trained with extensive annotation support from deaf teachers and students at Tianjin University of Technology. On the expression layer, visual-recognition and motion-generation technology drive a 3D digital human to perform sign language with high fidelity, including precise mouth-shape generation. The same Xiling platform later expanded into a broader one-stop digital-human infrastructure, but its sign-language anchor already proved that accessibility-focused AI media can operate at broadcast scale.

Data flow

Broadcast speech is transcribed by ASR, converted from Chinese text into sign-language codes through a specialized translation engine trained with deaf-community annotations, then rendered by a digital human whose motion, mouth shape, and expression are generated for synchronized viewing by hearing-impaired audiences.

Solution architecture

3 components · 2 layers
  1. Serving
    • AI Sign Language Anchor Delivers synchronized sign-language interpretation for live broadcasts, news, and event coverage.
    • AI Sign Language Platform Extends the same accessibility technology into text, video, and live scenarios across institutions such as hospitals, stations, and banks.
  2. Orchestration
    • Baidu AI Cloud Xiling Provides the digital-human platform and AI infrastructure used to build and run the sign-language anchor.

Architecture clues

  • The accessibility pipeline is explicitly layered into speech recognition, sign-language-code translation, and digital-human expression generation.
  • The solution later grew into a platformized digital-human product rather than staying a one-off event demo.
  • The translation layer required annotation help from nearly 20 deaf schools and 500-600 students.

Evidence from the source

Baidu AI Cloud Xiling's AI sign-language anchor has already provided sign-language services for live broadcasts and sporting events to 27 million hearing-impaired users. (original: 百度智能云曦灵打造的AI手语主播,已经为2700万听障用户提供直播及赛事的手语服务。)
Baidu's ASR accuracy exceeded 98%, while sign-language comprehensibility surpassed 85% after six rounds of iteration. (original: 百度智能云研发的ASR语音识别模型,准确性达到98%以上。...经过6次迭代,相关手语可懂度达到85%以上。)
CCTV News and Baidu AI Cloud jointly launched the first AI sign-language anchor, which later broadcast more than 200 news items and several live streams with over 100 million total views. (original: 由央视新闻与百度智能云曦灵联合打造的首个AI手语主播正式上线。...她已经播报了超过200条的新闻资讯,进行了数场直播,总观看次数超过1亿次。)
Mouth-shape generation accuracy exceeded 98.5%, and the same platform later supported broader sign-language and digital-human deployments. (original: 手语口型生成的准确度就超过98.5%。...百度智能云还相继推出了AI手语平台及一体机。)
The project decomposed the problem into hearing clearly, translating correctly, and expressing clearly through a digital human. (original: 首先是最底层,需把健听人说的语音翻译成汉语文本;其次是中层,需将汉语文本通过翻译引擎转换为手语码;最后是上层,通过数字人的动作融合算法,将手语码转换为手语。)
Transparency Evidence & methodology 96/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    CCTV News (央视新闻) customer story

    First-party
    https://cloud.baidu.com/customer/case/aishouyuzhubo.html
    Source type
    Vendor-published customer story
    Published
    May 15, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 15, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.