The field of voice conversion and text-to-speech synthesis is moving towards more controllable and expressive models. Researchers have made significant progress in disentangling speaker identity and linguistic content, allowing for more precise control over prosody and style. Notable papers include Discl-VC, StarVC, and Controllable Text-to-Speech Synthesis with Masked-Autoencoded Style-Rich Representation. In multilingual speech processing, researchers are focusing on adapting to low-resource languages and domains. Advances in few-shot learning methods and grapheme-coherent phonemic and prosodic annotation have improved language identification, automatic speech recognition, and speech translation. Noteworthy papers include Improving Multilingual Speech Models on ML-SUPERB 2.0 and Fewer Hallucinations, More Verification. The field of speaker recognition and diarization is also seeing significant improvements, with novel approaches to disentangle linguistic and speaker information. Multimodal information, including audio-visual signals, is becoming increasingly important for achieving state-of-the-art results. Noteworthy papers include LASPA and CASA-Net. Speech recognition is moving towards improving robustness and adaptability to diverse speech patterns. Researchers are leveraging pseudo-supervised learning methods, voice conversion, and fine-tuning of pre-trained models. Noteworthy papers include SuPseudo, Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic Dialect Identification, and Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning. Additionally, significant advancements are being made in speech processing, with a focus on improving speech translation, audio coding, and speech recognition. Researchers are exploring innovative architectures and techniques, such as joint speech recognition and translation using hierarchical efficient neural transducers. Noteworthy papers include HENT-SRT, SwitchCodec, and MFLA. The field of speech emotion recognition and prosody analysis is moving towards more inclusive and explainable models, with a focus on self-supervised learning approaches. Researchers are exploring new methods to identify semantically important segments in speech signals and analyzing prosodic characteristics associated with emotional states. Noteworthy papers include Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition and Investigating the Impact of Word Informativeness on Speech Emotion Recognition. Lastly, the field of natural language processing is moving towards a more equitable and inclusive approach to knowledge sharing across languages. Researchers are highlighting the importance of surfacing complementary information from non-English language editions and leveraging language transfer to improve low-resource language technologies. Noteworthy papers include WikiGap and Limited-Resource Adapters Are Regularizers, Not Linguists.