Search: “AudioCodes”

Apple Doesn’t Want You to Worry About the New Apple Watch's Listening Features

Apple Watch Series 12 and Ultra 4 add opt-in audio intelligence features that process microphone audio on-device via a new Secure Exclave.

The Apple Watch Series 12 and Ultra 4 ship with four opt-in audio intelligence features: Sound Recognition, Shazam music identification, Siri Recap conversation summaries, and Live Rewind 15-second transcription. Audio is held and processed in an isolated Secure Exclave buffer on the new S11 chips, with on-device speech recognition on iPhone producing a distilled transcript that foundation models in Private Cloud Compute then summarize. Apple says no raw audio is stored or accessible to the operating system, apps, the user, or Apple, and untransferred audio is automatically deleted.

WIRED · Security·updated Sep 10, 08:18 UTC · 6d ago·first Sep 9, 19:14 UTC · 6d agoAI industry 6 sources1

Watch out: Apple timepiece can grab snippets of conversation without both speakers' consent

Apple's Watch Series 12 Live Rewind and Siri Recap transcribe nearby conversations without bystander consent, drawing EFF criticism over all-party-consent laws.

Apple Watch Series 12's Audio Intelligence features on the S11 chip include Live Rewind, which transcribes the last 15 seconds of a conversation after a Digital Crown double-press, processing audio in a Secure Exclave and routing it to a nearby iPhone. Siri Recap generates AI summaries of daily conversations without retaining raw audio or attributing speakers. Apple says an audible chime and visual cue alert bystanders, but privacy advocates including the EFF note that 11 US states require all-party consent for recording and that bystanders have no practical way to opt in or decline.

The Register · Security·Sep 10, 19:50 UTC · 5d agoAI safety & security 2 sources

Cybersecurity jobs available right now: September 8, 2026

Help Net Security's September 8 job roundup lists open cybersecurity roles at AudioCodes, Garmin, Scotiabank, GDIT, Subway and others across seven countries.

The listing includes a CISO role at AudioCodes in Israel, a Combat Systems Cyber Engineer at Johns Hopkins Applied Physics Laboratory, cloud security engineering at Garmin, and an IAM Architect at Scotiabank. Other openings span penetration testing for NATO exercises at Spektrum in Belgium, SOC analyst at Orro Group in Australia, threat hunting at GDIT, and security operations at Subway in the USA. Roles range from CISO and architect to SOC analyst and penetration tester.

Help Net Security·Sep 8, 00:00 UTC · 8d agoIndustry

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.

LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:46 UTC · 23h agoAI research1

Inaudible sounds used to fingerprint browsers catch AliExpress red-handed

AliExpress was caught using inaudible audio signals to fingerprint visitors' browsers, an outdated but still-active cross-session tracking technique.

Ars Technica reports that AliExpress fingerprinted site visitors by sending inaudible sounds to their browsers, exposing a legacy audio-based fingerprinting technique in commercial use. The method allows persistent tracking despite the technique being widely considered outdated. The finding highlights that legacy tracking methods remain deployed on major e-commerce properties, raising privacy concerns for defenders and privacy teams.

Ars Technica · Security·Aug 24, 19:19 UTC · 22d agoResearch

X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.

X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

StepAudio 3 Gen Technical Report

StepAudio 3 Gen unifies TTS, voice design, music, and sound effects via discrete autoregressive modeling over RVQ tokens.

StepAudio 3 Gen is a general-purpose audio generation model covering zero-shot TTS, voice design, vocal generation, sound effects, music, vibe speech, and mixed audio in one framework. It uses discrete autoregressive modeling over residual vector quantization (RVQ) tokens rather than the diffusion Transformer paradigm, with a StepAudio Tokenizer representing audio at 12.5 Hz in a shared 16x2048 residual code space. Key design principles include interference-aware progressive pretraining, an RVQ Adaptor for multi-codebook acoustic representations, and shared discrete autoregressive modeling. The model reports state-of-the-art performance on TTS and voice design while retaining strong generation across speech, vocals, sound effects, and music.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

microsoft/VibeVoice-ASR-Streaming-7B — new model trending #27 on Hugging Face

Microsoft released VibeVoice-ASR-Streaming-7B, an open streaming ASR model with speaker attribution, custom hotwords, and support for 10 languages under MIT license.

Microsoft Research released VibeVoice-ASR-Streaming-7B on Hugging Face, a unified streaming speech recognition model that continuously transcribes who said what as speech arrives. The 7B model supports customized hotwords for domain-specific terms and 10 languages including Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. Code is available at github.com/microsoft/VibeVoice with a live demo, and a technical report is on arXiv (2609.02812). The model is licensed under MIT.

Hugging Face trending models·Sep 2, 15:46 UTC · 14d agoModel release1

What Did I Just Say? Self-Listening for Full-Duplex Speech Models

Researchers propose Self-Listening, a full-duplex speech approach feeding realized model speech back as input to improve interruption recovery.

Full-duplex spoken language models can listen and speak simultaneously, but asynchronous text generation, speech synthesis, and playback cause mismatches between what a model believes it said and what the user heard. The paper defines the resulting recovery problem as anchor interruption and proposes Self-Listening, which interleaves user speech, model text, and played speech as input streams. The authors also release AnchorSpeech, a benchmark with homogeneous training and test splits tracking which ordered response items were actually spoken. Experiments show self-listening models achieve better anchoring performance than full-duplex baselines.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Researchers distill a compact 82M-parameter Thai TTS from synthetic OmniVoice data, enabling on-device fixed-voice synthesis without reference audio.

The paper uses a large voice-cloning model (OmniVoice) as a synthetic data source to train Wayu-Paxa-TTS-Edge, an 82M-parameter fixed-voice Thai TTS student. The model achieves 68.2% Challenge-Set Keyword Accuracy (85.5% of Gemini 3.1), 91.4% pause precision, and CERs of 3.7% on Thai and 1.1% on English. It outperforms its teacher on pause placement and is open-sourced with its evaluation framework.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

Comfy-Org/YuE2 — new model trending #30 on Hugging Face

m-a-p's YuE2-3B music generation model and SheetSage2 audio encoder are repackaged in bf16 for ComfyUI and trending #30 on Hugging Face.

Comfy-Org published repackaged bf16 safetensors files for m-a-p's YuE2-3B model and its SheetSage2 audio encoder, organized into ComfyUI checkpoints and audio encoder folders. The repository links to the original m-a-p/YuE2-3B and m-a-p/SheetSage2 model pages and is currently trending #30 on Hugging Face.

Hugging Face trending models·updated Sep 12, 05:56 UTC · 4d ago·first Sep 11, 03:35 UTC · 5d agoModel release 7 sources

StepAudio 3 Realtime Technical Report

StepAudio 3 Realtime debuts an audio-language model with Think-While-Speaking reasoning, delivering full-duplex voice dialogue with top benchmark results.

StepAudio 3 Realtime is an audio-language foundation model built around a continuous listen-converse-think-act loop for real-time spoken interaction. Think-While-Speaking runs private reasoning in parallel with speech, reaching a 73.0 macro average on StepAudioChat in reasoning mode. The model reports 90.6 on MMSU, 98.9 overall on the Artificial Analysis Full-Duplex Bench, and 56.0% macro task success on tau-Voice. An integrated Voice Agent handles asynchronous tool execution without disrupting dialogue flow.

Hugging Face daily papers·Sep 11, 20:00 UTC · 4d agoAI research

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning

Kraken speech-to-speech translation model builds on Qwen3-8B with low-bitrate vector quantization and source-conditioned vocoding.

Kraken augments a pre-trained Qwen3-8B LLM with speech feature inputs and low-bitrate single-layer vector-quantized tokens trained to reconstruct SSL features. A separate token-to-waveform decoder, Autowave-X, is conditioned on source speech to improve non-linguistic transfer. Training used 150k hours of multilingual and multitask speech data. The model reportedly beats SeamlessM4T-Large v2 and Qwen2.5-Omni in translation quality and speaker/prosody transfer.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:41 UTC · 5d agoAI research1

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:26 UTC · 8d agoAI research1

StepAudio 3 Music Technical Report

StepAudio 3 Music introduces long-form text-controlled music generation using ABC-notation planning and flow-matching diffusion, ranking near the top music arena.

StepAudio 3 Music generates long-form, text-controlled music using a 50-Hz single-codebook tokenizer with 65,536 entries and a flow-matching diffusion Transformer over VAE latents. A Mixture-of-Experts autoregressive model first plans an arrangement in ABC notation (ABC-CoT) before predicting music tokens. With DPO fine-tuning, it tops AudioBox content and production quality scores and reaches Quality Elo 1105 on the Artificial Analysis Music Arena, behind Suno V5.5 and Mureka. Generation covers songs, accompaniment from dry vocals, and cover synthesis up to 5 minutes 30 seconds at 48-kHz output.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

Researchers introduced Brain2Semantics2Text, decoding sentence meaning from non-invasive MEG brain recordings via a semantic bottleneck, improving on prior Brain2Text methods.

The paper proposes Brain2Semantics2Text, a non-invasive speech decoding method that maps sentence-level magnetoencephalography (MEG) responses into a semantic embedding space and inverts those embeddings into natural language. Motivated by evidence that high-level semantic representations are distributed across cortex and evolve on slower timescales, the approach targets meaning rather than phonemes or words, avoiding the need for word-level alignment. The authors report improved sentence-level results compared to prior non-invasive Brain2Text methods despite the low signal-to-noise ratio of neural recordings.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research2

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

A survey catalogs inference-efficiency techniques for video and audiovisual LLMs, mapping bottlenecks in sampling, encoding, token reduction, and LLM decoding.

This survey covers inference-efficiency mechanisms for visual and audiovisual video LLMs, reporting reductions in parameters, FLOPs, latency, memory, and token counts. It organizes methods by pipeline stage, covering frame sampling, modality encoding, connector-level token reduction, and LLM prefilling and decoding for systems built since late 2022. The authors compile accuracy-cost comparisons under shared host models and input protocols, identify gaps in audiovisual efficiency and standardized evaluation, and maintain a public repository.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research

Do speech foundation models really learn words?

Researchers show via residualization that later layers of HuBERT and wav2vec 2.0 encode word identity and semantics independently of phonetic content.

The study argues that discriminative ability on words does not imply specialized word representations, since good word discrimination can be explained by phoneme encoding alone. By partialling out phoneme information using residualization, the authors show that later layers of HuBERT and wav2vec 2.0 encode words with reasonable fidelity independently of local phonetic content. Applying this disentanglement approach enhances higher-order linguistic information in word discovery tasks, informing analysis of speech foundation models used for recognition and speech tokens.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:47 UTC · 7d agoAI research

Skullcandy Dime 3 earbuds expose users to Bluetooth hijacking

CERT/CC warns Skullcandy Dime 3 earbuds accept silent Bluetooth pairings via CVE-2025-20701, letting nearby attackers hijack audio and microphone.

CERT/CC reports the Skullcandy Dime 3 (model S2DCW) running firmware 1.0.0.28 is affected by CVE-2025-20701, a high-severity missing-authentication flaw in the Airoha Bluetooth Audio SDK. An attacker in close range can pair without user interaction, then hijack audio playback, access the headset profile, and capture live microphone audio. Skullcandy fixed the issue in firmware 1.0.0.30, but existing units have no consumer-accessible update path via the app. The flaw was discovered by ERNW researchers and affects earbud and headphone products from multiple vendors; Apple patched it for Beats Studio Buds in June.

BleepingComputer·updated Sep 11, 04:16 UTC · 5d ago·first Sep 9, 21:02 UTC · 6d agoVulnerability 3 sourcesCVE-2025-20701

IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing

Researchers release IndicTriMix benchmarks and fine-tuned MuRIL and XLM-RoBERTa models for token-level language identification in tri-language code-mixed text.

The paper formulates token-level language identification in code-mixed text as a sequence labeling task and fine-tunes MuRIL and XLM-RoBERTa transformer models for Indian languages. It evaluates on Hindi, Gujarati, and Bengali configurations with manually annotated test sets and proposes two code-mixed generation approaches using parallel trilingual sentences. A public benchmark, annotated test sets, and fine-tuned models are released for reproducibility.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:36 UTC · 5d agoAI research1

Training-Free Speech-Centric Omni Understanding with Frozen VLMs

Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships. Existing omni models typically introduce dedicated audio encoders and rely on expensive audio-video-text training, tightly coupling omni capability to specific VLM backbones and potentially weakening their existing visual and reasoning abilities.…

Hugging Face daily papers·Aug 6, 20:00 UTC · Aug 6, 2026AI research

Safe word: What is it and why do you need one?

ESET recommends pre-agreed family safe words to counter AI voice-clone scams such as virtual kidnapping, as one-in-four Americans report receiving deepfake calls.

ESET outlines how scammers use just seconds of audio scraped from social media or work content to create convincing voice clones, with a Hiya report finding one-in-four Americans received a deepfake voice call in the past 12 months. Common schemes include virtual kidnapping calls mixing cloned voices with sobbing and background noise. A pre-agreed, non-OSINT-discoverable safe word, plus callback verification via known numbers and 2FA, reduces success rates of these frauds.

ESET WeLiveSecurity·Sep 9, 09:00 UTC · 7d agoPhishing & fraud in the wild

Gradium Launches Voice Design: Write a Prompt, Get a Brand New Synthetic Voice in Seconds

Gradium, a Kyutai spinout, launched Voice Design, generating custom synthetic voices from text descriptions in seconds across five languages.

Gradium, a Paris-based voice AI company spun out of Kyutai, launched Voice Design, which generates new synthetic voices from 1-500 character text descriptions in seconds without needing reference audio or speaker consent. The feature is live in the Gradium API and Studio, free on every plan including the free tier, and kept voices run on the standard streaming TTS endpoint at the same latency as catalog voices. Vendor-run blind pairwise listening tests across 7,627 comparisons report a 72.6% win rate, 13.6 points ahead of ElevenLabs at 59.0%, placing first in all five tested languages, with the largest margins on regional accents such as Quebecois French (97%).

MarkTechPost·Sep 9, 07:29 UTC · 7d agoAI industry

Domain-Incremental Learning for Multi-Channel Replay Speech Detection

First continual learning benchmark for multi-channel replay speech detection shows task-specific beamforming cuts catastrophic forgetting across 24 acoustic environments.

Researchers frame replay-attack detection for voice-controlled systems as domain-incremental learning over acoustic environments, evaluating a beamformer-based detector across all 24 environment orderings of the ReMASC corpus with five seeds. Naive sequential fine-tuning raises error rates on previously learned environments by 18.8 points, while elastic weight consolidation halves forgetting but loses plasticity and gradient projection memory is statistically indistinguishable from naive fine-tuning. A task-specific beamformer keeping one spatial front-end per environment significantly improves final and incremental accuracy, and the last environment in a sequence dominates final performance.

arXiv cs.CR·Sep 10, 08:03 UTC · 6d agoResearch1

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoModel release2

Amazon Prime Video’s new AI tech matches lips to dubbed audio

Amazon Prime Video launched AI lip-sync dubbing aligning actors' mouth movements with human-dubbed audio, starting with the English dub of Maxton Hall.

Prime Video's new feature combines AI and visual effects technologies to make lip movements match translated speech, currently available for seasons 1-2 of the German series Maxton Hall worldwide and planned for additional titles, with season 3 arriving December 9. Amazon previously piloted AI-aided dubbing in English and Spanish across 12 movies and shows last year. Meta and YouTube have recently launched similar AI auto-dubbing features with optional lip sync for creators.

The Verge · AI·Sep 9, 13:00 UTC · 7d agoAI industry

Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:55 UTC · 5d agoAI research2