Intelligent speech recognition is increasingly used in embedded systems, which is also seriously threatened by malicious speech spoofing attacks. Different from the conventional methods, this article proposes a segment-based anti-spoofing detection (SASD) method for the quick detection of spoofed speeches against embedded speech recognition, which focuses on the anti-spoofing features rather than the contexts of speeches and the voiceprints of speakers. The speeches are divided into word segments and silent segments. Based on constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> cepstral coefficients (CQCCs), a word CQCC (WCQCC) extraction is first designed for the word segments of speeches. Then, based on short-term zero crossing rate (ZCR), an average ZCR (AZCR) extraction is devised for the silent segments. Combining the WCQCC of word segments and AZCR of silent segments, a biased decision strategy is proposed to quickly determine whether a speech is spoofed. Based on ASVspoof 2021 datasets, extensive experiments are conducted to evaluate the effectiveness of the proposed method. Specifically, our SASD can improve the accuracy of anti-spoofing detection by up to 33.47% and save up to 69.10% of time overhead on embedded devices compared with the existing methods.