Tunable Single-frequency operation of a diode-pumped Vertical-External Cavity Laser at the Caesium D2 line

International audienceWe report on a diode-pumped vertical external-cavity surface-emitting laser emitting around 852 nm for Cesium atomic clocks experiments. We have designed a 7-quantum-well semiconductor structure optimized for low laser threshold. An output power of 330 mW was achieved for 1.1 W of incident pump power. Furthermore a compact setup was built for low-power single-requency emission. We obtained an output power of 17 mW in a single longitudinal mode, exhibiting both broad (9 nm) and continuous (14 GHz) tunability around the Cesium D2 line. The laser frequency has been stabilized on an atomic transition with residual frequency fluctuations ~ 300 kHz. Through a beatnote experiment the -3 dB laser linewidth has been measured to < 500 kHz over 10 ms

COMPACT AND ROBUST SINGLE-FREQUENCY DIODE-PUMPED VECSEL AT THE CESIUM D2 LINE FOR ATOMIC CLOCKS

This work reports on an optically-pumped vertical external-cavity surface­emitting laser emitting around 852 nm dedicated to atomic physics experiments with cold Cs atoms. The design of the semiconductor active structure has been optimized to provide a low threshold. A low-power diode-pumped compact prototype has been developed with improved stability. With this setup, we obtained a 17-mW single frequency emission exhibiting large tunability around the Cesium D2 line. The laser linewidth has been measured to less than 500 kHz on a 10 ms time

Crossing language identification: Multilingual ASR framework based on semantic dataset creation &amp; Wav2Vec 2.0

This study proposes an innovative methodology to enhance the performance of multilingual Automatic Speech Recognition (ASR) systems by capitalizing on the high semantic similarity between sentences across different languages and eliminating the requirement for Language Identification (LID). To achieve this, special bilingual datasets were created from the Mozzila Common Voices datasets in Spanish, Russian, and Portuguese. The process involves computing sentence embeddings using Language-agnostic BERT and selecting sentence pairs based on high and low cosine similarity. Subsequently, we train the Wav2vec 2.0 XLSR53 model on these datasets and assess its performance utilizing Character Error Rate (CER) and Word Error Rate (WER) metrics. The experimental results indicate that models trained on high-similarity samples consistently surpass their low-similarity counterparts, emphasizing the significance of high semantic similarity data selection for precise and dependable ASR performance. Furthermore, the elimination of LID contributes to a simplified system with reduced computational costs and the capacity for real-time text output. The findings of this research offer valuable insights for the development of more efficient and accurate multilingual ASR systems, particularly in real-time and on-device applications